Merge pull request #56 from vais-ral/PatchBased

LLT-ROF model

23 files changed, 1684 insertions, 66 deletions

diff --git a/Core/CMakeLists.txt b/Core/CMakeLists.txt
index f6cb3af..b4823d3 100644
--- a/Core/CMakeLists.txt
+++ b/Core/CMakeLists.txt
@@ -79,8 +79,7 @@ add_library(cilreg SHARED
 	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/TGV_core.c
 	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/Diffusion_core.c
 	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/Diffus4th_order_core.c
-	    #${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/LLT_model_core.c
-	    #${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/PatchBased_Regul_core.c
+	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/LLT_ROF_core.c
         ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/ROF_TV_core.c
         ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/FGP_dTV_core.c
         ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/TNV_core.c
@@ -128,6 +127,7 @@ if (CUDA_FOUND)
 	${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/TV_ROF_GPU_core.cu
 	${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/TV_FGP_GPU_core.cu
 	${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/TV_SB_GPU_core.cu
+	${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/LLT_ROF_GPU_core.cu
 	${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/TGV_GPU_core.cu
 	${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/dTV_FGP_GPU_core.cu
 	${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/NonlDiff_GPU_core.cu
diff --git a/Core/regularisers_CPU/LLT_ROF_core.c b/Core/regularisers_CPU/LLT_ROF_core.c
new file mode 100644
index 0000000..6dcf018
--- /dev/null
+++ b/Core/regularisers_CPU/LLT_ROF_core.c
@@ -0,0 +1,409 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include "LLT_ROF_core.h"
+#define EPS_LLT 0.01
+#define EPS_ROF 1.0e-5
+#define MAX(x, y) (((x) > (y)) ? (x) : (y))
+#define MIN(x, y) (((x) < (y)) ? (x) : (y))
+
+/*sign function*/
+int signLLT(float x) {
+    return (x > 0) - (x < 0);
+}
+
+/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty.
+ * 
+* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. 
+* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase 
+* lambdaLLT starting with smaller values. 
+*
+* Input Parameters:
+* 1. U0 - original noise image/volume
+* 2. lambdaROF - ROF-related regularisation parameter
+* 3. lambdaLLT - LLT-related regularisation parameter
+* 4. tau - time-marching step 
+* 5. iter - iterations number (for both models)
+*
+* Output:
+* Filtered/regularised image
+*
+* References: 
+* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.
+* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+*/
+
+float LLT_ROF_CPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int dimX, int dimY, int dimZ)
+{
+		int DimTotal, ll;
+		float *D1_LLT=NULL, *D2_LLT=NULL, *D3_LLT=NULL, *D1_ROF=NULL, *D2_ROF=NULL, *D3_ROF=NULL;
+		
+		DimTotal = dimX*dimY*dimZ;
+        
+        D1_ROF = calloc(DimTotal, sizeof(float));
+        D2_ROF = calloc(DimTotal, sizeof(float));
+        D3_ROF = calloc(DimTotal, sizeof(float));
+        
+        D1_LLT = calloc(DimTotal, sizeof(float));
+        D2_LLT = calloc(DimTotal, sizeof(float));
+        D3_LLT = calloc(DimTotal, sizeof(float));
+        
+        copyIm(Input, Output, dimX, dimY, dimZ); /* initialize  */
+       
+		for(ll = 0; ll < iterationsNumb; ll++) {            
+            if (dimZ == 1) {
+			/* 2D case */
+			/****************ROF******************/
+			 /* calculate first-order differences */
+            D1_func_ROF(Output, D1_ROF, dimX, dimY, dimZ);
+            D2_func_ROF(Output, D2_ROF, dimX, dimY, dimZ);
+            /****************LLT******************/
+            /* estimate second-order derrivatives */
+            der2D_LLT(Output, D1_LLT, D2_LLT, dimX, dimY, dimZ);
+            /* Joint update for ROF and LLT models */
+            Update2D_LLT_ROF(Input, Output, D1_LLT, D2_LLT, D1_ROF, D2_ROF, lambdaROF, lambdaLLT, tau, dimX, dimY, dimZ);
+            }
+            else {
+			/* 3D case */
+			/* calculate first-order differences */
+            D1_func_ROF(Output, D1_ROF, dimX, dimY, dimZ);
+            D2_func_ROF(Output, D2_ROF, dimX, dimY, dimZ);
+            D3_func_ROF(Output, D3_ROF, dimX, dimY, dimZ); 
+            /****************LLT******************/
+            /* estimate second-order derrivatives */
+            der3D_LLT(Output, D1_LLT, D2_LLT, D3_LLT, dimX, dimY, dimZ);
+            /* Joint update for ROF and LLT models */
+            Update3D_LLT_ROF(Input, Output, D1_LLT, D2_LLT, D3_LLT, D1_ROF, D2_ROF, D3_ROF, lambdaROF, lambdaLLT, tau, dimX, dimY, dimZ);
+			}
+        } /*end of iterations*/
+    free(D1_LLT);free(D2_LLT);free(D3_LLT);
+    free(D1_ROF);free(D2_ROF);free(D3_ROF);
+	return *Output;
+}
+
+/*************************************************************************/
+/**********************LLT-related functions *****************************/
+/*************************************************************************/
+float der2D_LLT(float *U, float *D1, float *D2, int dimX, int dimY, int dimZ)
+{
+	int i, j, index, i_p, i_m, j_m, j_p;
+	float dxx, dyy, denom_xx, denom_yy;
+#pragma omp parallel for shared(U,D1,D2) private(i, j, index, i_p, i_m, j_m, j_p, denom_xx, denom_yy, dxx, dyy)
+	for (i = 0; i<dimX; i++) {
+		for (j = 0; j<dimY; j++) {
+			index = j*dimX+i;
+			/* symmetric boundary conditions (Neuman) */
+			i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+			i_m = i - 1; if (i_m < 0) i_m = i + 1;
+			j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+			j_m = j - 1; if (j_m < 0) j_m = j + 1;
+
+			dxx = U[j*dimX+i_p] - 2.0f*U[index] + U[j*dimX+i_m];
+			dyy = U[j_p*dimX+i] - 2.0f*U[index] + U[j_m*dimX+i];
+
+			denom_xx = fabs(dxx) + EPS_LLT;
+			denom_yy = fabs(dyy) + EPS_LLT;
+
+			D1[index] = dxx / denom_xx;
+			D2[index] = dyy / denom_yy;
+		}
+	}
+	return 1;
+}
+
+float der3D_LLT(float *U, float *D1, float *D2, float *D3, int dimX, int dimY, int dimZ)
+ {
+ 	int i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, index;
+ 	float dxx, dyy, dzz, denom_xx, denom_yy, denom_zz;
+ #pragma omp parallel for shared(U,D1,D2,D3) private(i, j, index, k, i_p, i_m, j_m, j_p, k_p, k_m, denom_xx, denom_yy, denom_zz, dxx, dyy, dzz)
+ 	for (i = 0; i<dimX; i++) {
+ 		for (j = 0; j<dimY; j++) {
+ 			for (k = 0; k<dimZ; k++) {
+				/* symmetric boundary conditions (Neuman) */
+				i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+				i_m = i - 1; if (i_m < 0) i_m = i + 1;
+				j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+				j_m = j - 1; if (j_m < 0) j_m = j + 1;
+ 				k_p = k + 1; if (k_p == dimZ) k_p = k - 1;
+ 				k_m = k - 1; if (k_m < 0) k_m = k + 1;
+				
+				index = (dimX*dimY)*k + j*dimX+i;
+ 
+ 				dxx = U[(dimX*dimY)*k + j*dimX+i_p] - 2.0f*U[index] + U[(dimX*dimY)*k + j*dimX+i_m];
+ 				dyy = U[(dimX*dimY)*k + j_p*dimX+i] - 2.0f*U[index] + U[(dimX*dimY)*k + j_m*dimX+i];
+ 				dzz = U[(dimX*dimY)*k_p + j*dimX+i] - 2.0f*U[index] + U[(dimX*dimY)*k_m + j*dimX+i];
+ 
+ 				denom_xx = fabs(dxx) + EPS_LLT;
+ 				denom_yy = fabs(dyy) + EPS_LLT;
+ 				denom_zz = fabs(dzz) + EPS_LLT;
+ 
+ 				D1[index] = dxx / denom_xx;
+ 				D2[index] = dyy / denom_yy;
+ 				D3[index] = dzz / denom_zz;
+ 			}
+ 		}
+ 	}
+ 	return 1;
+ }
+
+/*************************************************************************/
+/**********************ROF-related functions *****************************/
+/*************************************************************************/
+
+/* calculate differences 1 */
+float D1_func_ROF(float *A, float *D1, int dimX, int dimY, int dimZ)
+{
+    float NOMx_1, NOMy_1, NOMy_0, NOMz_1, NOMz_0, denom1, denom2,denom3, T1;
+    int i,j,k,i1,i2,k1,j1,j2,k2,index;
+    
+    if (dimZ > 1) {
+#pragma omp parallel for shared (A, D1, dimX, dimY, dimZ) private(index, i, j, k, i1, j1, k1, i2, j2, k2, NOMx_1,NOMy_1,NOMy_0,NOMz_1,NOMz_0,denom1,denom2,denom3,T1)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+                for(k=0; k<dimZ; k++) {
+					index = (dimX*dimY)*k + j*dimX+i;
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = A[(dimX*dimY)*k + j1*dimX + i] - A[index]; /* x+ */
+                    NOMy_1 = A[(dimX*dimY)*k + j*dimX + i1] - A[index]; /* y+ */
+                    /*NOMx_0 = (A[(i)*dimY + j] - A[(i2)*dimY + j]); */  /* x- */
+                    NOMy_0 = A[index] - A[(dimX*dimY)*k + j*dimX + i2]; /* y- */
+                    
+                    NOMz_1 = A[(dimX*dimY)*k1 + j*dimX + i] - A[index]; /* z+ */
+                    NOMz_0 = A[index] - A[(dimX*dimY)*k2 + j*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMx_1*NOMx_1;
+                    denom2 = 0.5f*(signLLT(NOMy_1) + signLLT(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5f*(signLLT(NOMz_1) + signLLT(NOMz_0))*(MIN(fabs(NOMz_1),fabs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T1 = sqrt(denom1 + denom2 + denom3 + EPS_ROF);
+                    D1[index] = NOMx_1/T1;
+                }}}
+    }
+    else {
+#pragma omp parallel for shared (A, D1, dimX, dimY) private(i, j, i1, j1, i2, j2,NOMx_1,NOMy_1,NOMy_0,denom1,denom2,T1,index)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+				index = j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = A[j1*dimX + i] - A[index]; /* x+ */
+                NOMy_1 = A[j*dimX + i1] - A[index]; /* y+ */
+                /*NOMx_0 = (A[(i)*dimY + j] - A[(i2)*dimY + j]); */ /* x- */
+                NOMy_0 = A[index] - A[(j)*dimX + i2]; /* y- */
+                
+                denom1 = NOMx_1*NOMx_1;
+                denom2 = 0.5f*(signLLT(NOMy_1) + signLLT(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+                denom2 = denom2*denom2;
+                T1 = sqrtf(denom1 + denom2 + EPS_ROF);
+                D1[index] = NOMx_1/T1;
+            }}
+    }
+    return *D1;
+}
+/* calculate differences 2 */
+float D2_func_ROF(float *A, float *D2, int dimX, int dimY, int dimZ)
+{
+    float NOMx_1, NOMy_1, NOMx_0, NOMz_1, NOMz_0, denom1, denom2, denom3, T2;
+    int i,j,k,i1,i2,k1,j1,j2,k2,index;
+    
+    if (dimZ > 1) {
+#pragma omp parallel for shared (A, D2, dimX, dimY, dimZ) private(index, i, j, k, i1, j1, k1, i2, j2, k2,  NOMx_1, NOMy_1, NOMx_0, NOMz_1, NOMz_0, denom1, denom2, denom3, T2)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+                for(k=0; k<dimZ; k++) {
+					index = (dimX*dimY)*k + j*dimX+i;
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;
+                    
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = A[(dimX*dimY)*k + (j1)*dimX + i] - A[index]; /* x+ */
+                    NOMy_1 = A[(dimX*dimY)*k + (j)*dimX + i1] - A[index]; /* y+ */
+                    NOMx_0 = A[index] - A[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                    NOMz_1 = A[(dimX*dimY)*k1 + j*dimX + i] - A[index]; /* z+ */
+                    NOMz_0 = A[index] - A[(dimX*dimY)*k2 + (j)*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMy_1*NOMy_1;
+                    denom2 = 0.5f*(signLLT(NOMx_1) + signLLT(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5f*(signLLT(NOMz_1) + signLLT(NOMz_0))*(MIN(fabs(NOMz_1),fabs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T2 = sqrtf(denom1 + denom2 + denom3 + EPS_ROF);
+                    D2[index] = NOMy_1/T2;
+                }}}
+    }
+    else {
+#pragma omp parallel for shared (A, D2, dimX, dimY) private(i, j, i1, j1, i2, j2, NOMx_1,NOMy_1,NOMx_0,denom1,denom2,T2,index)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+				index = j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = A[j1*dimX + i] - A[index]; /* x+ */
+                NOMy_1 = A[j*dimX + i1] - A[index]; /* y+ */
+                NOMx_0 = A[index] - A[j2*dimX + i]; /* x- */
+                /*NOMy_0 = A[(i)*dimY + j] - A[(i)*dimY + j2]; */  /* y- */
+                
+                denom1 = NOMy_1*NOMy_1;
+                denom2 = 0.5f*(signLLT(NOMx_1) + signLLT(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                denom2 = denom2*denom2;
+                T2 = sqrtf(denom1 + denom2 + EPS_ROF);
+                D2[index] = NOMy_1/T2;
+            }}
+    }
+    return *D2;
+}
+
+/* calculate differences 3 */
+float D3_func_ROF(float *A, float *D3, int dimY, int dimX, int dimZ)
+{
+    float NOMx_1, NOMy_1, NOMx_0, NOMy_0, NOMz_1, denom1, denom2, denom3, T3;
+    int index,i,j,k,i1,i2,k1,j1,j2,k2;
+    
+#pragma omp parallel for shared (A, D3, dimX, dimY, dimZ) private(index, i, j, k, i1, j1, k1, i2, j2, k2,  NOMx_1, NOMy_1, NOMy_0, NOMx_0, NOMz_1, denom1, denom2, denom3, T3)
+    for(j=0; j<dimY; j++) {
+        for(i=0; i<dimX; i++) {
+            for(k=0; k<dimZ; k++) {
+				index = (dimX*dimY)*k + j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                k2 = k - 1; if (k2 < 0) k2 = k+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = A[(dimX*dimY)*k + (j1)*dimX + i] - A[index]; /* x+ */
+                NOMy_1 = A[(dimX*dimY)*k + (j)*dimX + i1] - A[index]; /* y+ */
+                NOMy_0 = A[index] - A[(dimX*dimY)*k + (j)*dimX + i2]; /* y- */
+                NOMx_0 = A[index] - A[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                NOMz_1 = A[(dimX*dimY)*k1 + j*dimX + i] - A[index]; /* z+ */
+                /*NOMz_0 = A[(dimX*dimY)*k + (i)*dimY + j] - A[(dimX*dimY)*k2 + (i)*dimY + j]; */ /* z- */
+                
+                denom1 = NOMz_1*NOMz_1;
+                denom2 = 0.5f*(signLLT(NOMx_1) + signLLT(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                denom2 = denom2*denom2;
+                denom3 = 0.5f*(signLLT(NOMy_1) + signLLT(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+                denom3 = denom3*denom3;
+                T3 = sqrtf(denom1 + denom2 + denom3 + EPS_ROF);
+                D3[index] = NOMz_1/T3;
+            }}}
+    return *D3;
+}
+
+/*************************************************************************/
+/**********************ROF-LLT-related functions *************************/
+/*************************************************************************/
+
+float Update2D_LLT_ROF(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D1_ROF, float *D2_ROF, float lambdaROF, float lambdaLLT, float tau, int dimX, int dimY, int dimZ)
+{
+	int i, j, index, i_p, i_m, j_m, j_p;
+	float div, laplc, dxx, dyy, dv1, dv2;
+#pragma omp parallel for shared(U,U0) private(i, j, index, i_p, i_m, j_m, j_p, laplc, div, dxx, dyy, dv1, dv2)
+	for (i = 0; i<dimX; i++) {
+		for (j = 0; j<dimY; j++) {
+			index = j*dimX+i;
+			/* symmetric boundary conditions (Neuman) */
+			i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+			i_m = i - 1; if (i_m < 0) i_m = i + 1;
+			j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+			j_m = j - 1; if (j_m < 0) j_m = j + 1;
+			
+			/*LLT-related part*/
+			dxx = D1_LLT[j*dimX+i_p] - 2.0f*D1_LLT[index] + D1_LLT[j*dimX+i_m];
+			dyy = D2_LLT[j_p*dimX+i] - 2.0f*D2_LLT[index] + D2_LLT[j_m*dimX+i];
+			laplc = dxx + dyy; /*build Laplacian*/
+			
+			/*ROF-related part*/
+			dv1 = D1_ROF[index] - D1_ROF[j_m*dimX + i];
+            dv2 = D2_ROF[index] - D2_ROF[j*dimX + i_m];
+			div = dv1 + dv2; /*build Divirgent*/
+            
+			/*combine all into one cost function to minimise */
+            U[index] += tau*(lambdaROF*(div) - lambdaLLT*(laplc) - (U[index] - U0[index]));
+		}
+	}
+	return *U;
+}
+
+float Update3D_LLT_ROF(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D3_LLT, float *D1_ROF, float *D2_ROF, float *D3_ROF, float lambdaROF, float lambdaLLT, float tau, int dimX, int dimY, int dimZ)
+{
+	int i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, index;
+	float div, laplc, dxx, dyy, dzz, dv1, dv2, dv3;
+#pragma omp parallel for shared(U,U0) private(i, j, k, index, i_p, i_m, j_m, j_p, k_p, k_m, laplc, div, dxx, dyy, dzz, dv1, dv2, dv3)
+ 	for (i = 0; i<dimX; i++) {
+ 		for (j = 0; j<dimY; j++) {
+ 			for (k = 0; k<dimZ; k++) {
+				/* symmetric boundary conditions (Neuman) */
+				i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+				i_m = i - 1; if (i_m < 0) i_m = i + 1;
+				j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+				j_m = j - 1; if (j_m < 0) j_m = j + 1;
+ 				k_p = k + 1; if (k_p == dimZ) k_p = k - 1;
+ 				k_m = k - 1; if (k_m < 0) k_m = k + 1;
+			
+				index = (dimX*dimY)*k + j*dimX+i;
+			
+				/*LLT-related part*/
+				dxx = D1_LLT[(dimX*dimY)*k + j*dimX+i_p] - 2.0f*D1_LLT[index] + D1_LLT[(dimX*dimY)*k + j*dimX+i_m];
+				dyy = D2_LLT[(dimX*dimY)*k + j_p*dimX+i] - 2.0f*D2_LLT[index] + D2_LLT[(dimX*dimY)*k + j_m*dimX+i];
+				dzz = D3_LLT[(dimX*dimY)*k_p + j*dimX+i] - 2.0f*D3_LLT[index] + D3_LLT[(dimX*dimY)*k_m + j*dimX+i];
+				laplc = dxx + dyy + dzz; /*build Laplacian*/
+			
+				/*ROF-related part*/
+				dv1 = D1_ROF[index] - D1_ROF[(dimX*dimY)*k + j_m*dimX+i];
+				dv2 = D2_ROF[index] - D2_ROF[(dimX*dimY)*k + j*dimX+i_m];
+				dv3 = D3_ROF[index] - D3_ROF[(dimX*dimY)*k_m + j*dimX+i];
+				div = dv1 + dv2 + dv3; /*build Divirgent*/
+            
+				/*combine all into one cost function to minimise */
+				U[index] += tau*(lambdaROF*(div) - lambdaLLT*(laplc) - (U[index] - U0[index]));
+			}
+		}
+	}
+	return *U;
+}
+
diff --git a/Core/regularisers_CPU/LLT_ROF_core.h b/Core/regularisers_CPU/LLT_ROF_core.h
new file mode 100644
index 0000000..65d25cd
--- /dev/null
+++ b/Core/regularisers_CPU/LLT_ROF_core.h
@@ -0,0 +1,65 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty.
+ * 
+* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. 
+* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase 
+* lambdaLLT starting with smaller values. 
+*
+* Input Parameters:
+* 1. U0 - original noise image/volume
+* 2. lambdaROF - ROF-related regularisation parameter
+* 3. lambdaLLT - LLT-related regularisation parameter
+* 4. tau - time-marching step 
+* 5. iter - iterations number (for both models)
+*
+* Output:
+* Filtered/regularised image
+*
+* References: 
+* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.
+* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+*/
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float LLT_ROF_CPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
+
+CCPI_EXPORT float der2D_LLT(float *U, float *D1, float *D2, int dimX, int dimY, int dimZ);
+CCPI_EXPORT float der3D_LLT(float *U, float *D1, float *D2, float *D3, int dimX, int dimY, int dimZ);
+
+CCPI_EXPORT float D1_func_ROF(float *A, float *D1, int dimY, int dimX, int dimZ);
+CCPI_EXPORT float D2_func_ROF(float *A, float *D2, int dimY, int dimX, int dimZ);
+CCPI_EXPORT float D3_func_ROF(float *A, float *D3, int dimY, int dimX, int dimZ);
+
+CCPI_EXPORT float Update2D_LLT_ROF(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D1_ROF, float *D2_ROF, float lambdaROF, float lambdaLLT, float tau, int dimX, int dimY, int dimZ);
+CCPI_EXPORT float Update3D_LLT_ROF(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D3_LLT, float *D1_ROF, float *D2_ROF, float *D3_ROF, float lambdaROF, float lambdaLLT, float tau, int dimX, int dimY, int dimZ);
+#ifdef __cplusplus
+}
+#endif
diff --git a/Core/regularisers_CPU/ROF_TV_core.c b/Core/regularisers_CPU/ROF_TV_core.c
index 213645d..e89774f 100644
--- a/Core/regularisers_CPU/ROF_TV_core.c
+++ b/Core/regularisers_CPU/ROF_TV_core.c
@@ -60,13 +60,13 @@ float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iteratio
     copyIm(Input, Output, dimX, dimY, dimZ);
         
     /* start TV iterations */
-    for(i=0; i < iterationsNumb; i++) {           
+    for(i=0; i < iterationsNumb; i++) {
             
             /* calculate differences */
             D1_func(Output, D1, dimX, dimY, dimZ);
-            D2_func(Output, D2, dimX, dimY, dimZ);           
-            if (dimZ > 1) D3_func(Output, D3, dimX, dimY, dimZ); 			
-            TV_kernel(D1, D2, D3, Output, Input, lambdaPar, tau, dimX, dimY, dimZ);  		          
+            D2_func(Output, D2, dimX, dimY, dimZ);
+            if (dimZ > 1) D3_func(Output, D3, dimX, dimY, dimZ); 
+            TV_kernel(D1, D2, D3, Output, Input, lambdaPar, tau, dimX, dimY, dimZ);
 		}           
     free(D1);free(D2); free(D3);
     return *Output;
@@ -283,7 +283,7 @@ float TV_kernel(float *D1, float *D2, float *D3, float *B, float *A, float lambd
                 dv1 = D1[index] - D1[j2*dimX + i];
                 dv2 = D2[index] - D2[j*dimX + i2];                
 
-                B[index] =  B[index] + tau*(lambda*(dv1 + dv2) - (B[index] - A[index]));                
+                B[index] =  B[index] + tau*(lambda*(dv1 + dv2) - (B[index] - A[index]));
             }}
     }
     return *B;
diff --git a/Core/regularisers_GPU/LLT_ROF_GPU_core.cu b/Core/regularisers_GPU/LLT_ROF_GPU_core.cu
new file mode 100644
index 0000000..70c9295
--- /dev/null
+++ b/Core/regularisers_GPU/LLT_ROF_GPU_core.cu
@@ -0,0 +1,483 @@
+ /*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/ 
+
+#include "LLT_ROF_GPU_core.h"
+
+/* CUDA implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty.
+ * 
+* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. 
+* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase 
+* lambdaLLT starting with smaller values. 
+*
+* Input Parameters:
+* 1. U0 - original noise image/volume
+* 2. lambdaROF - ROF-related regularisation parameter
+* 3. lambdaLLT - LLT-related regularisation parameter
+* 4. tau - time-marching step 
+* 5. iter - iterations number (for both models)
+*
+* Output:
+* Filtered/regularised image
+*
+* References: 
+* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.
+* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+*/
+
+#define CHECK(call)                                                            \
+{                                                                              \
+    const cudaError_t error = call;                                            \
+    if (error != cudaSuccess)                                                  \
+    {                                                                          \
+        fprintf(stderr, "Error: %s:%d, ", __FILE__, __LINE__);                 \
+        fprintf(stderr, "code: %d, reason: %s\n", error,                       \
+                cudaGetErrorString(error));                                    \
+        exit(1);                                                               \
+    }                                                                          \
+}
+    
+#define BLKXSIZE 8
+#define BLKYSIZE 8
+#define BLKZSIZE 8
+    
+#define BLKXSIZE2D 16
+#define BLKYSIZE2D 16
+
+
+#define EPS_LLT 0.01
+#define EPS_ROF 1.0e-5
+
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+
+#define MAX(x, y) (((x) > (y)) ? (x) : (y))
+#define MIN(x, y) (((x) < (y)) ? (x) : (y))
+
+__host__ __device__ int signLLT (float x)
+{
+        return (x > 0) - (x < 0);
+}        
+   
+/*************************************************************************/
+/**********************LLT-related functions *****************************/
+/*************************************************************************/
+__global__ void der2D_LLT_kernel(float *U, float *D1, float *D2, int dimX, int dimY)
+    {
+		int i_p, i_m, j_m, j_p;
+		float dxx, dyy, denom_xx, denom_yy;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {
+            
+			/* symmetric boundary conditions (Neuman) */
+			i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+			i_m = i - 1; if (i_m < 0) i_m = i + 1;
+			j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+			j_m = j - 1; if (j_m < 0) j_m = j + 1;
+
+			dxx = U[j*dimX+i_p] - 2.0f*U[index] + U[j*dimX+i_m];
+			dyy = U[j_p*dimX+i] - 2.0f*U[index] + U[j_m*dimX+i];
+
+			denom_xx = abs(dxx) + EPS_LLT;
+			denom_yy = abs(dyy) + EPS_LLT;
+
+			D1[index] = dxx / denom_xx;
+			D2[index] = dyy / denom_yy;
+		}
+	}
+	
+__global__ void der3D_LLT_kernel(float* U, float *D1, float *D2, float *D3, int dimX, int dimY, int dimZ)
+    {
+		int i_p, i_m, j_m, j_p, k_p, k_m;
+		float dxx, dyy, dzz, denom_xx, denom_yy, denom_zz;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+			
+        /* symmetric boundary conditions (Neuman) */
+ 		i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+ 		i_m = i - 1; if (i_m < 0) i_m = i + 1;
+ 		j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+ 		j_m = j - 1; if (j_m < 0) j_m = j + 1;
+ 		k_p = k + 1; if (k_p == dimZ) k_p = k - 1;
+ 		k_m = k - 1; if (k_m < 0) k_m = k + 1;
+        
+      	int index = (dimX*dimY)*k + j*dimX+i;
+      	
+      	dxx = U[(dimX*dimY)*k + j*dimX+i_p] - 2.0f*U[index] + U[(dimX*dimY)*k + j*dimX+i_m];
+ 		dyy = U[(dimX*dimY)*k + j_p*dimX+i] - 2.0f*U[index] + U[(dimX*dimY)*k + j_m*dimX+i];
+ 		dzz = U[(dimX*dimY)*k_p + j*dimX+i] - 2.0f*U[index] + U[(dimX*dimY)*k_m + j*dimX+i];
+ 
+ 		denom_xx = abs(dxx) + EPS_LLT;
+ 		denom_yy = abs(dyy) + EPS_LLT;
+ 		denom_zz = abs(dzz) + EPS_LLT;
+ 
+ 		D1[index] = dxx / denom_xx;
+ 		D2[index] = dyy / denom_yy;
+ 		D3[index] = dzz / denom_zz;
+		}
+	}
+
+/*************************************************************************/
+/**********************ROF-related functions *****************************/
+/*************************************************************************/
+
+/* first-order differences 1 */
+__global__ void D1_func2D_ROF_kernel(float* Input, float* D1, int N, int M)
+    {
+		int i1, j1, i2;
+		float NOMx_1,NOMy_1,NOMy_0,denom1,denom2,T1;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + N*j;        
+        
+        if ((i >= 0) && (i < N) && (j >= 0) && (j < M)) {
+            
+            /* boundary conditions (Neumann reflections) */
+                i1 = i + 1; if (i1 >= N) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= M) j1 = j-1;
+		
+		     /* Forward-backward differences */
+                NOMx_1 = Input[j1*N + i] - Input[index]; /* x+ */
+                NOMy_1 = Input[j*N + i1] - Input[index]; /* y+ */                
+                NOMy_0 = Input[index] - Input[j*N + i2]; /* y- */
+                
+                denom1 = NOMx_1*NOMx_1;
+                denom2 = 0.5f*(signLLT((float)NOMy_1) + signLLT((float)NOMy_0))*(MIN(abs((float)NOMy_1),abs((float)NOMy_0)));
+                denom2 = denom2*denom2;
+                T1 = sqrt(denom1 + denom2 + EPS_ROF);
+                D1[index] = NOMx_1/T1;
+		}		
+	}
+	
+/* differences 2 */
+__global__ void D2_func2D_ROF_kernel(float* Input, float* D2, int N, int M)      
+    {
+		int i1, j1, j2;
+		float NOMx_1,NOMy_1,NOMx_0,denom1,denom2,T2;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + N*j;        
+        
+        if ((i >= 0) && (i < (N)) && (j >= 0) && (j < (M))) {
+            
+            /* boundary conditions (Neumann reflections) */
+                i1 = i + 1; if (i1 >= N) i1 = i-1;
+                j1 = j + 1; if (j1 >= M) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1; 
+		
+                /* Forward-backward differences */
+                NOMx_1 = Input[j1*N + i] - Input[index]; /* x+ */
+                NOMy_1 = Input[j*N + i1] - Input[index]; /* y+ */
+                NOMx_0 = Input[index] - Input[j2*N + i]; /* x- */
+                
+                denom1 = NOMy_1*NOMy_1;
+                denom2 = 0.5f*(signLLT((float)NOMx_1) + signLLT((float)NOMx_0))*(MIN(abs((float)NOMx_1),abs((float)NOMx_0)));
+                denom2 = denom2*denom2;
+                T2 = sqrt(denom1 + denom2 + EPS_ROF);
+                D2[index] = NOMy_1/T2;	
+		}		
+	}
+
+ 
+    /* differences 1 */
+__global__ void D1_func3D_ROF_kernel(float* Input, float* D1, int dimX, int dimY, int dimZ)      
+    {
+		float NOMx_1, NOMy_1, NOMy_0, NOMz_1, NOMz_0, denom1, denom2,denom3, T1;
+		int i1,i2,k1,j1,j2,k2;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+      	int index = (dimX*dimY)*k + j*dimX+i;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+            
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = Input[(dimX*dimY)*k + j1*dimX + i] - Input[index]; /* x+ */
+                    NOMy_1 = Input[(dimX*dimY)*k + j*dimX + i1] - Input[index]; /* y+ */                    
+                    NOMy_0 = Input[index] - Input[(dimX*dimY)*k + j*dimX + i2]; /* y- */
+                    
+                    NOMz_1 = Input[(dimX*dimY)*k1 + j*dimX + i] - Input[index]; /* z+ */
+                    NOMz_0 = Input[index] - Input[(dimX*dimY)*k2 + j*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMx_1*NOMx_1;
+                    denom2 = 0.5*(signLLT(NOMy_1) + signLLT(NOMy_0))*(MIN(abs(NOMy_1),abs(NOMy_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5*(signLLT(NOMz_1) + signLLT(NOMz_0))*(MIN(abs(NOMz_1),abs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T1 = sqrt(denom1 + denom2 + denom3 + EPS_ROF);
+                    D1[index] = NOMx_1/T1;	
+		}		
+	}      
+
+    /* differences 2 */
+    __global__ void D2_func3D_ROF_kernel(float* Input, float* D2, int dimX, int dimY, int dimZ)      
+    {
+		float NOMx_1, NOMy_1, NOMx_0, NOMz_1, NOMz_0, denom1, denom2, denom3, T2;
+		int i1,i2,k1,j1,j2,k2;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+      	int index = (dimX*dimY)*k + j*dimX+i;     
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;
+                    
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = Input[(dimX*dimY)*k + (j1)*dimX + i] - Input[index]; /* x+ */
+                    NOMy_1 = Input[(dimX*dimY)*k + (j)*dimX + i1] - Input[index]; /* y+ */
+                    NOMx_0 = Input[index] - Input[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                    NOMz_1 = Input[(dimX*dimY)*k1 + j*dimX + i] - Input[index]; /* z+ */
+                    NOMz_0 = Input[index] - Input[(dimX*dimY)*k2 + (j)*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMy_1*NOMy_1;
+                    denom2 = 0.5*(signLLT(NOMx_1) + signLLT(NOMx_0))*(MIN(abs(NOMx_1),abs(NOMx_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5*(signLLT(NOMz_1) + signLLT(NOMz_0))*(MIN(abs(NOMz_1),abs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T2 = sqrt(denom1 + denom2 + denom3 + EPS_ROF);
+                    D2[index] = NOMy_1/T2;
+		}
+	}
+	
+	  /* differences 3 */
+    __global__ void D3_func3D_ROF_kernel(float* Input, float* D3, int dimX, int dimY, int dimZ)      
+    {
+		float NOMx_1, NOMy_1, NOMx_0, NOMy_0, NOMz_1, denom1, denom2, denom3, T3;
+		int i1,i2,k1,j1,j2,k2;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+      	int index = (dimX*dimY)*k + j*dimX+i;     
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+
+				i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                k2 = k - 1; if (k2 < 0) k2 = k+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = Input[(dimX*dimY)*k + (j1)*dimX + i] - Input[index]; /* x+ */
+                NOMy_1 = Input[(dimX*dimY)*k + (j)*dimX + i1] - Input[index]; /* y+ */
+                NOMy_0 = Input[index] - Input[(dimX*dimY)*k + (j)*dimX + i2]; /* y- */
+                NOMx_0 = Input[index] - Input[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                NOMz_1 = Input[(dimX*dimY)*k1 + j*dimX + i] - Input[index]; /* z+ */
+               
+                denom1 = NOMz_1*NOMz_1;
+                denom2 = 0.5*(signLLT(NOMx_1) + signLLT(NOMx_0))*(MIN(abs(NOMx_1),abs(NOMx_0)));
+                denom2 = denom2*denom2;
+                denom3 = 0.5*(signLLT(NOMy_1) + signLLT(NOMy_0))*(MIN(abs(NOMy_1),abs(NOMy_0)));
+                denom3 = denom3*denom3;
+                T3 = sqrt(denom1 + denom2 + denom3 + EPS_ROF);
+                D3[index] = NOMz_1/T3;
+		}
+	}
+/*************************************************************************/
+/**********************ROF-LLT-related functions *************************/
+/*************************************************************************/
+
+__global__ void Update2D_LLT_ROF_kernel(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D1_ROF, float *D2_ROF, float lambdaROF, float lambdaLLT, float tau, int dimX, int dimY)
+{
+		
+		int i_p, i_m, j_m, j_p;
+		float div, laplc, dxx, dyy, dv1, dv2;
+	
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {
+            
+			/* symmetric boundary conditions (Neuman) */
+			i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+			i_m = i - 1; if (i_m < 0) i_m = i + 1;
+			j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+			j_m = j - 1; if (j_m < 0) j_m = j + 1;
+
+			index = j*dimX+i;
+					
+			/*LLT-related part*/
+			dxx = D1_LLT[j*dimX+i_p] - 2.0f*D1_LLT[index] + D1_LLT[j*dimX+i_m];
+			dyy = D2_LLT[j_p*dimX+i] - 2.0f*D2_LLT[index] + D2_LLT[j_m*dimX+i];
+			laplc = dxx + dyy; /*build Laplacian*/
+			/*ROF-related part*/
+			dv1 = D1_ROF[index] - D1_ROF[j_m*dimX + i];
+            dv2 = D2_ROF[index] - D2_ROF[j*dimX + i_m];
+			div = dv1 + dv2; /*build Divirgent*/
+            
+			/*combine all into one cost function to minimise */
+            U[index] += tau*(lambdaROF*(div) - lambdaLLT*(laplc) - (U[index] - U0[index]));
+		}
+}
+
+__global__ void Update3D_LLT_ROF_kernel(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D3_LLT, float *D1_ROF, float *D2_ROF, float *D3_ROF, float lambdaROF, float lambdaLLT, float tau, int dimX, int dimY, int dimZ)
+{
+	int i_p, i_m, j_m, j_p, k_p, k_m;
+	float div, laplc, dxx, dyy, dzz, dv1, dv2, dv3;
+	
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+			
+			/* symmetric boundary conditions (Neuman) */
+			i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+			i_m = i - 1; if (i_m < 0) i_m = i + 1;
+			j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+			j_m = j - 1; if (j_m < 0) j_m = j + 1;
+			k_p = k + 1; if (k_p == dimZ) k_p = k - 1;
+			k_m = k - 1; if (k_m < 0) k_m = k + 1;
+        
+			int index = (dimX*dimY)*k + j*dimX+i;
+      	
+			/*LLT-related part*/
+			dxx = D1_LLT[(dimX*dimY)*k + j*dimX+i_p] - 2.0f*D1_LLT[index] + D1_LLT[(dimX*dimY)*k + j*dimX+i_m];
+			dyy = D2_LLT[(dimX*dimY)*k + j_p*dimX+i] - 2.0f*D2_LLT[index] + D2_LLT[(dimX*dimY)*k + j_m*dimX+i];
+			dzz = D3_LLT[(dimX*dimY)*k_p + j*dimX+i] - 2.0f*D3_LLT[index] + D3_LLT[(dimX*dimY)*k_m + j*dimX+i];
+			laplc = dxx + dyy + dzz; /*build Laplacian*/
+			
+			/*ROF-related part*/
+			dv1 = D1_ROF[index] - D1_ROF[(dimX*dimY)*k + j_m*dimX+i];
+            dv2 = D2_ROF[index] - D2_ROF[(dimX*dimY)*k + j*dimX+i_m];
+            dv3 = D3_ROF[index] - D3_ROF[(dimX*dimY)*k_m + j*dimX+i];
+			div = dv1 + dv2 + dv3; /*build Divirgent*/
+            
+			/*combine all into one cost function to minimise */
+            U[index] += tau*(lambdaROF*(div) - lambdaLLT*(laplc) - (U[index] - U0[index]));
+        }
+}
+
+/*******************************************************************/
+/************************ HOST FUNCTION ****************************/
+/*******************************************************************/
+
+extern "C" void LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int N, int M, int Z)
+{
+	    // set up device
+		int dev = 0;
+		int DimTotal;
+		DimTotal = N*M*Z;
+		CHECK(cudaSetDevice(dev));
+        float *d_input, *d_update;
+        float *D1_LLT=NULL, *D2_LLT=NULL, *D3_LLT=NULL, *D1_ROF=NULL, *D2_ROF=NULL, *D3_ROF=NULL;
+        
+	if (Z == 0) {Z = 1;}
+	
+        CHECK(cudaMalloc((void**)&d_input,DimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&d_update,DimTotal*sizeof(float)));
+        
+        CHECK(cudaMalloc((void**)&D1_LLT,DimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&D2_LLT,DimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&D3_LLT,DimTotal*sizeof(float)));
+        
+        CHECK(cudaMalloc((void**)&D1_ROF,DimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&D2_ROF,DimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&D3_ROF,DimTotal*sizeof(float)));
+        
+        CHECK(cudaMemcpy(d_input,Input,DimTotal*sizeof(float),cudaMemcpyHostToDevice));
+        CHECK(cudaMemcpy(d_update,Input,DimTotal*sizeof(float),cudaMemcpyHostToDevice));
+        
+    if (Z == 1) {
+			// TV - 2D case
+            dim3 dimBlock(BLKXSIZE2D,BLKYSIZE2D);
+            dim3 dimGrid(idivup(N,BLKXSIZE2D), idivup(M,BLKYSIZE2D));
+             
+            for(int n=0; n < iterationsNumb; n++) {
+                /****************ROF******************/
+				/* calculate first-order differences */
+                D1_func2D_ROF_kernel<<<dimGrid,dimBlock>>>(d_update, D1_ROF, N, M);
+                CHECK(cudaDeviceSynchronize());
+				D2_func2D_ROF_kernel<<<dimGrid,dimBlock>>>(d_update, D2_ROF, N, M);
+                CHECK(cudaDeviceSynchronize());                
+                /****************LLT******************/
+                 /* estimate second-order derrivatives */
+				der2D_LLT_kernel<<<dimGrid,dimBlock>>>(d_update, D1_LLT, D2_LLT, N, M);
+				/* Joint update for ROF and LLT models */
+				Update2D_LLT_ROF_kernel<<<dimGrid,dimBlock>>>(d_input, d_update, D1_LLT, D2_LLT, D1_ROF, D2_ROF, lambdaROF, lambdaLLT, tau, N, M);
+                CHECK(cudaDeviceSynchronize());
+            }
+    }
+    else {
+			// 3D case
+            dim3 dimBlock(BLKXSIZE,BLKYSIZE,BLKZSIZE);
+            dim3 dimGrid(idivup(N,BLKXSIZE), idivup(M,BLKYSIZE),idivup(Z,BLKXSIZE));
+           
+            for(int n=0; n < iterationsNumb; n++) {
+                /****************ROF******************/
+				/* calculate first-order differences */
+                D1_func3D_ROF_kernel<<<dimGrid,dimBlock>>>(d_update, D1_ROF, N, M, Z);
+                CHECK(cudaDeviceSynchronize());
+				D2_func3D_ROF_kernel<<<dimGrid,dimBlock>>>(d_update, D2_ROF, N, M, Z);
+                CHECK(cudaDeviceSynchronize());        
+                D3_func3D_ROF_kernel<<<dimGrid,dimBlock>>>(d_update, D3_ROF, N, M, Z);
+                CHECK(cudaDeviceSynchronize());        
+                /****************LLT******************/
+                 /* estimate second-order derrivatives */
+				der3D_LLT_kernel<<<dimGrid,dimBlock>>>(d_update, D1_LLT, D2_LLT, D3_LLT, N, M, Z);
+				/* Joint update for ROF and LLT models */
+				Update3D_LLT_ROF_kernel<<<dimGrid,dimBlock>>>(d_input, d_update, D1_LLT, D2_LLT, D3_LLT, D1_ROF, D2_ROF, D3_ROF, lambdaROF, lambdaLLT, tau, N, M, Z);
+                CHECK(cudaDeviceSynchronize());
+            }
+    }        
+        CHECK(cudaMemcpy(Output,d_update,DimTotal*sizeof(float),cudaMemcpyDeviceToHost));
+        CHECK(cudaFree(d_input));
+        CHECK(cudaFree(d_update));
+        CHECK(cudaFree(D1_LLT));
+        CHECK(cudaFree(D2_LLT));
+        CHECK(cudaFree(D3_LLT));
+        CHECK(cudaFree(D1_ROF));
+        CHECK(cudaFree(D2_ROF));
+        CHECK(cudaFree(D3_ROF));
+}
diff --git a/Core/regularisers_GPU/LLT_ROF_GPU_core.h b/Core/regularisers_GPU/LLT_ROF_GPU_core.h
new file mode 100644
index 0000000..4a19d09
--- /dev/null
+++ b/Core/regularisers_GPU/LLT_ROF_GPU_core.h
@@ -0,0 +1,8 @@
+#ifndef __ROFLLTGPU_H__
+#define __ROFLLTGPU_H__
+#include "CCPiDefines.h"
+#include <stdio.h>
+
+extern "C" CCPI_EXPORT void LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int N, int M, int Z);
+
+#endif 
diff --git a/Readme.md b/Readme.md
index 2144d3e..2cfde4e 100644
--- a/Readme.md
+++ b/Readme.md
@@ -19,9 +19,10 @@
 1. Rudin-Osher-Fatemi (ROF) Total Variation (explicit PDE minimisation scheme) **2D/3D CPU/GPU** (Ref. *1*)
 2. Fast-Gradient-Projection (FGP) Total Variation **2D/3D CPU/GPU** (Ref. *2*)
 3. Split-Bregman (SB) Total Variation **2D/3D CPU/GPU** (Ref. *5*)
-4. Total Generilised Variation (TGV) model **2D CPU/GPU** (Ref. *6*)
+4. Total Generalised Variation (TGV) model for higher-order regularisation **2D CPU/GPU** (Ref. *6*)
 5. Linear and nonlinear diffusion (explicit PDE minimisation scheme) **2D/3D CPU/GPU** (Ref. *8*)
 6. Anisotropic Fourth-Order Diffusion (explicit PDE minimisation) **2D/3D CPU/GPU** (Ref. *9*)
+7. A joint ROF-LLT (Lysaker-Lundervold-Tai) model for higher-order regularisation **2D/3D CPU/GPU** (Ref. *10,11*)
 
 ### Multi-channel (denoising):
 1. Fast-Gradient-Projection (FGP) Directional Total Variation **2D/3D CPU/GPU** (Ref. *3,4,2*)
@@ -53,24 +54,36 @@
 	compileGPU_mex.m % to compile GPU modules (see instructions in the file)
 ```
 
-### References:
-1. [Rudin, L.I., Osher, S. and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4), pp.259-268.](https://doi.org/10.1016/0167-2789(92)90242-F)*
+### References to implemented methods:
+1. [Rudin, L.I., Osher, S. and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4), pp.259-268.](https://www.sciencedirect.com/science/article/pii/016727899290242F)
 
-2. [Beck, A. and Teboulle, M., 2009. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11), pp.2419-2434.](https://doi.org/10.1109/TIP.2009.2028250)*
+2. [Beck, A. and Teboulle, M., 2009. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11), pp.2419-2434.](https://doi.org/10.1109/TIP.2009.2028250)
 
-3. [Ehrhardt, M.J. and Betcke, M.M., 2016. Multicontrast MRI reconstruction with structure-guided total variation. SIAM Journal on Imaging Sciences, 9(3), pp.1084-1106.](https://doi.org/10.1137/15M1047325)*
+3. [Ehrhardt, M.J. and Betcke, M.M., 2016. Multicontrast MRI reconstruction with structure-guided total variation. SIAM Journal on Imaging Sciences, 9(3), pp.1084-1106.](https://doi.org/10.1137/15M1047325)
 
-4. [Kazantsev, D., Jørgensen, J.S., Andersen, M., Lionheart, W.R., Lee, P.D. and Withers, P.J., 2018. Joint image reconstruction method with correlative multi-channel prior for X-ray spectral computed tomography. Inverse Problems, 34(6)](https://doi.org/10.1088/1361-6420/aaba86)* **Results can be reproduced using the following** [SOFTWARE](https://github.com/dkazanc/multi-channel-X-ray-CT)
+4. [Kazantsev, D., Jørgensen, J.S., Andersen, M., Lionheart, W.R., Lee, P.D. and Withers, P.J., 2018. Joint image reconstruction method with correlative multi-channel prior for X-ray spectral computed tomography. Inverse Problems, 34(6)](https://doi.org/10.1088/1361-6420/aaba86) **Results can be reproduced using the following** [SOFTWARE](https://github.com/dkazanc/multi-channel-X-ray-CT)
 
-5. [Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343.](https://doi.org/10.1137/080725891)*
+5. [Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343.](https://doi.org/10.1137/080725891)
 
 6. [Bredies, K., Kunisch, K. and Pock, T., 2010. Total generalized variation. SIAM Journal on Imaging Sciences, 3(3), pp.492-526.](https://doi.org/10.1137/090769521)
 
 7. [Duran, J., Moeller, M., Sbert, C. and Cremers, D., 2016. Collaborative total variation: a general framework for vectorial TV models. SIAM Journal on Imaging Sciences, 9(1), pp.116-151.](https://doi.org/10.1137/15M102873X)
 
-8. [Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.](https://doi.org/10.1109/83.661192)*
+8. [Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.](https://doi.org/10.1109/83.661192)
 
-9. [Hajiaboli, M.R., 2011. An anisotropic fourth-order diffusion filter for image noise removal. International Journal of Computer Vision, 92(2), pp.177-191.](https://doi.org/10.1007/s11263-010-0330-1)*
+9. [Hajiaboli, M.R., 2011. An anisotropic fourth-order diffusion filter for image noise removal. International Journal of Computer Vision, 92(2), pp.177-191.](https://doi.org/10.1007/s11263-010-0330-1)
+
+10. [Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.](https://doi.org/10.1109/TIP.2003.819229)
+
+11. [Kazantsev, D., Guo, E., Phillion, A.B., Withers, P.J. and Lee, P.D., 2017. Model-based iterative reconstruction using higher-order regularization of dynamic synchrotron data. Measurement Science and Technology, 28(9), p.094004.](https://doi.org/10.1088/1361-6501/aa7fa8)
+
+### References to Software:
+* If software has been used, please refer to [11], however the supporting publication is in progress. 
+
+### Applications:
+
+* [Regularised FISTA-type iterative reconstruction algorithm for X-ray tomographic reconstruction with highly inaccurate measurements (MATLAB code)](https://github.com/dkazanc/FISTA-tomo)
+* [Joint image reconstruction method with correlative multi-channel prior for X-ray spectral computed tomography](https://github.com/dkazanc/multi-channel-X-ray-CT)
 
 ### License:
 [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0)
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
index 9a65e37..5cc47b3 100644
--- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -6,11 +6,13 @@ addpath(Path1);
 addpath(Path2);
 
 N = 512; 
-slices = 30;
+slices = 15;
 vol3D = zeros(N,N,slices, 'single');
+Ideal3D = zeros(N,N,slices, 'single');
 Im = double(imread('lena_gray_512.tif'))/255;  % loading image
 for i = 1:slices
 vol3D(:,:,i) = Im + .05*randn(size(Im)); 
+Ideal3D(:,:,i) = Im;
 end
 vol3D(vol3D < 0) = 0;
 figure; imshow(vol3D(:,:,15), [0 1]); title('Noisy image');
@@ -23,39 +25,71 @@ tau_rof = 0.0025; % time-marching constant
 iter_rof = 300; % number of ROF iterations
 tic; u_rof = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof); toc; 
 energyfunc_val_rof = TV_energy(single(u_rof),single(vol3D),lambda_reg, 1);  % get energy function value
-figure; imshow(u_rof(:,:,15), [0 1]); title('ROF-TV denoised volume (CPU)');
+rmse_rof = (RMSE(Ideal3D(:),u_rof(:)));
+fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rof);
+figure; imshow(u_rof(:,:,7), [0 1]); title('ROF-TV denoised volume (CPU)');
 %%
 % fprintf('Denoise a volume using the ROF-TV model (GPU) \n');
 % tau_rof = 0.0025; % time-marching constant 
 % iter_rof = 300; % number of ROF iterations
 % tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_reg, iter_rof, tau_rof); toc;
-% figure; imshow(u_rofG(:,:,15), [0 1]); title('ROF-TV denoised volume (GPU)');
+% rmse_rofG = (RMSE(Ideal3D(:),u_rofG(:)));
+% fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rofG);
+% figure; imshow(u_rofG(:,:,7), [0 1]); title('ROF-TV denoised volume (GPU)');
 %%
 fprintf('Denoise a volume using the FGP-TV model (CPU) \n');
 iter_fgp = 300; % number of FGP iterations
 epsil_tol =  1.0e-05; % tolerance
 tic; u_fgp = FGP_TV(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; 
 energyfunc_val_fgp = TV_energy(single(u_fgp),single(vol3D),lambda_reg, 1); % get energy function value
-figure; imshow(u_fgp(:,:,15), [0 1]); title('FGP-TV denoised volume (CPU)');
+rmse_fgp = (RMSE(Ideal3D(:),u_fgp(:)));
+fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgp);
+figure; imshow(u_fgp(:,:,7), [0 1]); title('FGP-TV denoised volume (CPU)');
 %%
 % fprintf('Denoise a volume using the FGP-TV model (GPU) \n');
 % iter_fgp = 300; % number of FGP iterations
 % epsil_tol =  1.0e-05; % tolerance
 % tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; 
-% figure; imshow(u_fgpG(:,:,15), [0 1]); title('FGP-TV denoised volume (GPU)');
+% rmse_fgpG = (RMSE(Ideal3D(:),u_fgpG(:)));
+% fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgpG);
+% figure; imshow(u_fgpG(:,:,7), [0 1]); title('FGP-TV denoised volume (GPU)');
 %%
 fprintf('Denoise a volume using the SB-TV model (CPU) \n');
 iter_sb = 150; % number of SB iterations
 epsil_tol =  1.0e-05; % tolerance
 tic; u_sb = SB_TV(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; 
 energyfunc_val_sb = TV_energy(single(u_sb),single(vol3D),lambda_reg, 1);  % get energy function value
-figure; imshow(u_sb(:,:,15), [0 1]); title('SB-TV denoised volume (CPU)');
+rmse_sb = (RMSE(Ideal3D(:),u_sb(:)));
+fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sb);
+figure; imshow(u_sb(:,:,7), [0 1]); title('SB-TV denoised volume (CPU)');
 %%
 % fprintf('Denoise a volume using the SB-TV model (GPU) \n');
 % iter_sb = 150; % number of SB iterations
 % epsil_tol =  1.0e-05; % tolerance
 % tic; u_sbG = SB_TV_GPU(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; 
-% figure; imshow(u_sbG(:,:,15), [0 1]); title('SB-TV denoised volume (GPU)');
+% rmse_sbG = (RMSE(Ideal3D(:),u_sbG(:)));
+% fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sbG);
+% figure; imshow(u_sbG(:,:,7), [0 1]); title('SB-TV denoised volume (GPU)');
+%%
+fprintf('Denoise a volume using the ROF-LLT model (CPU) \n');
+lambda_ROF = lambda_reg; % ROF regularisation parameter
+lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter
+iter_LLT = 300; % iterations 
+tau_rof_llt = 0.0025; % time-marching constant 
+tic; u_rof_llt = LLT_ROF(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; 
+rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt(:)));
+fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt);
+figure; imshow(u_rof_llt(:,:,7), [0 1]); title('ROF-LLT denoised volume (CPU)');
+%%
+% fprintf('Denoise a volume using the ROF-LLT model (GPU) \n');
+% lambda_ROF = lambda_reg; % ROF regularisation parameter
+% lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter
+% iter_LLT = 300; % iterations 
+% tau_rof_llt = 0.0025; % time-marching constant 
+% tic; u_rof_llt_g = LLT_ROF_GPU(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; 
+% rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt_g(:)));
+% fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt);
+% figure; imshow(u_rof_llt_g(:,:,7), [0 1]); title('ROF-LLT denoised volume (GPU)');
 %%
 fprintf('Denoise a volume using Nonlinear-Diffusion model (CPU) \n');
 iter_diff = 300; % number of diffusion iterations
@@ -63,7 +97,9 @@ lambda_regDiff = 0.025; % regularisation for the diffusivity
 sigmaPar = 0.015; % edge-preserving parameter
 tau_param = 0.025; % time-marching constant 
 tic; u_diff = NonlDiff(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; 
-figure; imshow(u_diff(:,:,15), [0 1]); title('Diffusion denoised volume (CPU)');
+rmse_diff = (RMSE(Ideal3D(:),u_diff(:)));
+fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff);
+figure; imshow(u_diff(:,:,7), [0 1]); title('Diffusion denoised volume (CPU)');
 %%
 % fprintf('Denoise a volume using Nonlinear-Diffusion model (GPU) \n');
 % iter_diff = 300; % number of diffusion iterations
@@ -71,7 +107,9 @@ figure; imshow(u_diff(:,:,15), [0 1]); title('Diffusion denoised volume (CPU)');
 % sigmaPar = 0.015; % edge-preserving parameter
 % tau_param = 0.025; % time-marching constant 
 % tic; u_diff_g = NonlDiff_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; 
-% figure; imshow(u_diff_g(:,:,15), [0 1]); title('Diffusion denoised volume (GPU)');
+% rmse_diff = (RMSE(Ideal3D(:),u_diff_g(:)));
+% fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff);
+% figure; imshow(u_diff_g(:,:,7), [0 1]); title('Diffusion denoised volume (GPU)');
 %%
 fprintf('Denoise using Fourth-order anisotropic diffusion model (CPU) \n');
 iter_diff = 300; % number of diffusion iterations
@@ -79,7 +117,9 @@ lambda_regDiff = 3.5; % regularisation for the diffusivity
 sigmaPar = 0.02; % edge-preserving parameter
 tau_param = 0.0015; % time-marching constant 
 tic; u_diff4 = Diffusion_4thO(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; 
-figure; imshow(u_diff4(:,:,15), [0 1]); title('Diffusion 4thO denoised volume (CPU)');
+rmse_diff4 = (RMSE(Ideal3D(:),u_diff4(:)));
+fprintf('%s %f \n', 'RMSE error for Anis.Diff of 4th order is:', rmse_diff4);
+figure; imshow(u_diff4(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (CPU)');
 %%
 % fprintf('Denoise using Fourth-order anisotropic diffusion model (GPU) \n');
 % iter_diff = 300; % number of diffusion iterations
@@ -87,7 +127,9 @@ figure; imshow(u_diff4(:,:,15), [0 1]); title('Diffusion 4thO denoised volume (C
 % sigmaPar = 0.02; % edge-preserving parameter
 % tau_param = 0.0015; % time-marching constant 
 % tic; u_diff4_g = Diffusion_4thO_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; 
-% figure; imshow(u_diff4_g(:,:,15), [0 1]); title('Diffusion 4thO denoised volume (GPU)');
+% rmse_diff4 = (RMSE(Ideal3D(:),u_diff4_g(:)));
+% fprintf('%s %f \n', 'RMSE error for Anis.Diff of 4th order is:', rmse_diff4);
+% figure; imshow(u_diff4_g(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (GPU)');
 %%
 
 %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
@@ -105,7 +147,7 @@ iter_fgp = 300; % number of FGP iterations
 epsil_tol =  1.0e-05; % tolerance
 eta =  0.2; % Reference image gradient smoothing constant
 tic; u_fgp_dtv = FGP_dTV(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
-figure; imshow(u_fgp_dtv(:,:,15), [0 1]); title('FGP-dTV denoised volume (CPU)');
+figure; imshow(u_fgp_dtv(:,:,7), [0 1]); title('FGP-dTV denoised volume (CPU)');
 %%
 fprintf('Denoise a volume using the FGP-dTV model (GPU) \n');
 
@@ -121,5 +163,5 @@ iter_fgp = 300; % number of FGP iterations
 epsil_tol =  1.0e-05; % tolerance
 eta =  0.2; % Reference image gradient smoothing constant
 tic; u_fgp_dtv_g = FGP_dTV_GPU(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
-figure; imshow(u_fgp_dtv_g(:,:,15), [0 1]); title('FGP-dTV denoised volume (GPU)');
+figure; imshow(u_fgp_dtv_g(:,:,7), [0 1]); title('FGP-dTV denoised volume (GPU)');
 %%
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index 3f0ca54..d11bc63 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -79,6 +79,26 @@ figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)');
 % fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV_gpu);
 % figure; imshow(u_tgv_gpu, [0 1]); title('TGV denoised image (GPU)');
 %%
+fprintf('Denoise using the ROF-LLT model (CPU) \n');
+lambda_ROF = lambda_reg; % ROF regularisation parameter
+lambda_LLT = lambda_reg*0.45; % LLT regularisation parameter
+iter_LLT = 1; % iterations 
+tau_rof_llt = 0.0025; % time-marching constant 
+tic; u_rof_llt = LLT_ROF(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; 
+rmseROFLLT = (RMSE(u_rof_llt(:),Im(:)));
+fprintf('%s %f \n', 'RMSE error for TGV is:', rmseROFLLT);
+figure; imshow(u_rof_llt, [0 1]); title('ROF-LLT denoised image (CPU)');
+%%
+% fprintf('Denoise using the ROF-LLT model (GPU) \n');
+% lambda_ROF = lambda_reg; % ROF regularisation parameter
+% lambda_LLT = lambda_reg*0.45; % LLT regularisation parameter
+% iter_LLT = 500; % iterations 
+% tau_rof_llt = 0.0025; % time-marching constant 
+% tic; u_rof_llt_g = LLT_ROF_GPU(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; 
+% rmseROFLLT_g = (RMSE(u_rof_llt_g(:),Im(:)));
+% fprintf('%s %f \n', 'RMSE error for TGV is:', rmseROFLLT_g);
+% figure; imshow(u_rof_llt_g, [0 1]); title('ROF-LLT denoised image (GPU)');
+%%
 fprintf('Denoise using Nonlinear-Diffusion model (CPU) \n');
 iter_diff = 800; % number of diffusion iterations
 lambda_regDiff = 0.025; % regularisation for the diffusivity 
diff --git a/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m b/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m
index 8acc1b7..064b416 100644
--- a/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m
+++ b/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m
@@ -14,44 +14,60 @@ cd regularisers_CPU
 
 Pathmove = sprintf(['..' fsep 'installed' fsep], 1i);
 
-fprintf('%s \n', 'Compiling CPU regularisers...');
+fprintf('%s \n', '<<<<<<<<<<<Compiling CPU regularisers>>>>>>>>>>>>>');
+
+fprintf('%s \n', 'Compiling ROF-TV...');
 mex ROF_TV.c ROF_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('ROF_TV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling FGP-TV...');
 mex FGP_TV.c FGP_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('FGP_TV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling SB-TV...');
 mex SB_TV.c SB_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('SB_TV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling dFGP-TV...');
 mex FGP_dTV.c FGP_dTV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('FGP_dTV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling TNV...');
 mex TNV.c TNV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('TNV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling NonLinear Diffusion...');
 mex NonlDiff.c Diffusion_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('NonlDiff.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling Anisotropic diffusion of higher order...');
 mex Diffusion_4thO.c Diffus4th_order_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('Diffusion_4thO.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling TGV...');
 mex TGV.c TGV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('TGV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling ROF-LLT...');
+mex LLT_ROF.c LLT_ROF_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('LLT_ROF.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling additional tools...');
 mex TV_energy.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('TV_energy.mex*',Pathmove);
 
 %############Inpainters##############%
+fprintf('%s \n', 'Compiling Nonlinear/Linear diffusion inpainting...');
 mex NonlDiff_Inp.c Diffusion_Inpaint_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('NonlDiff_Inp.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling Nonlocal marching method for inpaiting...');
 mex NonlocalMarching_Inpaint.c NonlocalMarching_Inpaint_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('NonlocalMarching_Inpaint.mex*',Pathmove);
 
-delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* Diffus4th_order_core* TGV_core* CCPiDefines.h
+delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* Diffus4th_order_core* TGV_core* LLT_ROF_core* CCPiDefines.h
 delete Diffusion_Inpaint_core* NonlocalMarching_Inpaint_core*
-fprintf('%s \n', 'Regularisers successfully compiled!');
+fprintf('%s \n', '<<<<<<< Regularisers successfully compiled! >>>>>>>');
 
 pathA2 = sprintf(['..' fsep '..' fsep], 1i);
 cd(pathA2);
diff --git a/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m b/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
index ea1ad7d..1b59dc2 100644
--- a/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
+++ b/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
@@ -5,7 +5,7 @@
 % not sure if openmp is enabled after the compilation. 
 
 % Here I present two ways how software can be compiled, if you have some
-% other suggestions please contact me at dkazanc@hotmail.com 
+% other suggestions/remarks please contact me at dkazanc@hotmail.com 
 % >>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
 fsep = '/';
@@ -22,38 +22,54 @@ cd regularisers_CPU
 
 Pathmove = sprintf(['..' fsep 'installed' fsep], 1i);
 
-fprintf('%s \n', 'Compiling CPU regularisers...');
+fprintf('%s \n', '<<<<<<<<<<<Compiling CPU regularisers>>>>>>>>>>>>>');
+
+fprintf('%s \n', 'Compiling ROF-TV...');
 mex ROF_TV.c ROF_TV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('ROF_TV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling FGP-TV...');
 mex FGP_TV.c FGP_TV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('FGP_TV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling SB-TV...');
 mex SB_TV.c SB_TV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('SB_TV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling dFGP-TV...');
 mex FGP_dTV.c FGP_dTV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('FGP_dTV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling TNV...');
 mex TNV.c TNV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('TNV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling NonLinear Diffusion...');
 mex NonlDiff.c Diffusion_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('NonlDiff.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling Anisotropic diffusion of higher order...');
 mex Diffusion_4thO.c Diffus4th_order_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('Diffusion_4thO.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling TGV...');
 mex TGV.c TGV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('TGV.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling ROF-LLT...');
+mex LLT_ROF.c LLT_ROF_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('LLT_ROF.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling additional tools...');
 mex TV_energy.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('TV_energy.mex*',Pathmove);
 
 %############Inpainters##############%
+fprintf('%s \n', 'Compiling Nonlinear/Linear diffusion inpainting...');
 mex NonlDiff_Inp.c Diffusion_Inpaint_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('NonlDiff_Inp.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling Nonlocal marching method for inpaiting...');
 mex NonlocalMarching_Inpaint.c NonlocalMarching_Inpaint_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('NonlocalMarching_Inpaint.mex*',Pathmove);
 
@@ -87,6 +103,8 @@ fprintf('%s \n', 'Regularisers successfully compiled!');
 % movefile('Diffusion_4thO.mex*',Pathmove);
 % mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" TGV.c TGV_core.c utils.c
 % movefile('TGV.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" LLT_ROF.c LLT_ROF_core.c utils.c
+% movefile('LLT_ROF.mex*',Pathmove);
 % mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" TV_energy.c utils.c
 % movefile('TV_energy.mex*',Pathmove);
 % mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" NonlDiff_Inp.c Diffusion_Inpaint_core.c utils.c
diff --git a/Wrappers/Matlab/mex_compile/compileGPU_mex.m b/Wrappers/Matlab/mex_compile/compileGPU_mex.m
index 003c6ec..e0311ea 100644
--- a/Wrappers/Matlab/mex_compile/compileGPU_mex.m
+++ b/Wrappers/Matlab/mex_compile/compileGPU_mex.m
@@ -9,8 +9,8 @@
 
 % Tested on Ubuntu 16.04/MATLAB 2016b/cuda7.5/gcc4.9
 
-% It HAS NOT been tested on Windows, please contact me if you'll be able to
-% install software on Windows and I greatefully include it into the release. 
+% Installation HAS NOT been tested on Windows, please contact me if you'll be able to
+% install software on Windows and I gratefully include it into the master release. 
 
 fsep = '/';
 
@@ -24,36 +24,49 @@ cd regularisers_GPU
 
 Pathmove = sprintf(['..' fsep 'installed' fsep], 1i);
 
-fprintf('%s \n', 'Compiling GPU regularisers (CUDA)...');
+fprintf('%s \n', '<<<<<<<<<<<Compiling GPU regularisers (CUDA)>>>>>>>>>>>>>');
+
+fprintf('%s \n', 'Compiling ROF-TV...');
 !/usr/local/cuda/bin/nvcc -O0 -c TV_ROF_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
 mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu ROF_TV_GPU.cpp TV_ROF_GPU_core.o
 movefile('ROF_TV_GPU.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling FGP-TV...');
 !/usr/local/cuda/bin/nvcc -O0 -c TV_FGP_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
 mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu FGP_TV_GPU.cpp TV_FGP_GPU_core.o
 movefile('FGP_TV_GPU.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling SB-TV...');
 !/usr/local/cuda/bin/nvcc -O0 -c TV_SB_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
 mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu SB_TV_GPU.cpp TV_SB_GPU_core.o
 movefile('SB_TV_GPU.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling TGV...');
 !/usr/local/cuda/bin/nvcc -O0 -c TGV_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
 mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu TGV_GPU.cpp TGV_GPU_core.o
 movefile('TGV_GPU.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling dFGP-TV...');
 !/usr/local/cuda/bin/nvcc -O0 -c dTV_FGP_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
 mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu FGP_dTV_GPU.cpp dTV_FGP_GPU_core.o
 movefile('FGP_dTV_GPU.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling NonLinear Diffusion...');
 !/usr/local/cuda/bin/nvcc -O0 -c NonlDiff_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
 mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu NonlDiff_GPU.cpp NonlDiff_GPU_core.o
 movefile('NonlDiff_GPU.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling Anisotropic diffusion of higher order...');
 !/usr/local/cuda/bin/nvcc -O0 -c Diffus_4thO_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
 mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu Diffusion_4thO_GPU.cpp Diffus_4thO_GPU_core.o
 movefile('Diffusion_4thO_GPU.mex*',Pathmove);
 
-delete TV_ROF_GPU_core* TV_FGP_GPU_core* TV_SB_GPU_core* dTV_FGP_GPU_core* NonlDiff_GPU_core* Diffus_4thO_GPU_core* TGV_GPU_core* CCPiDefines.h
+fprintf('%s \n', 'Compiling ROF-LLT...');
+!/usr/local/cuda/bin/nvcc -O0 -c LLT_ROF_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
+mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu LLT_ROF_GPU.cpp LLT_ROF_GPU_core.o
+movefile('LLT_ROF_GPU.mex*',Pathmove);
+
+delete TV_ROF_GPU_core* TV_FGP_GPU_core* TV_SB_GPU_core* dTV_FGP_GPU_core* NonlDiff_GPU_core* Diffus_4thO_GPU_core* TGV_GPU_core* LLT_ROF_GPU_core* CCPiDefines.h
 fprintf('%s \n', 'All successfully compiled!');
 
 pathA2 = sprintf(['..' fsep '..' fsep], 1i);
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c
new file mode 100644
index 0000000..81b717d
--- /dev/null
+++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c
@@ -0,0 +1,81 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "LLT_ROF_core.h"
+
+/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty.
+* 
+* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. 
+* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase 
+* lambdaLLT starting with smaller values. 
+*
+* Input Parameters:
+* 1. U0 - original noise image/volume
+* 2. lambdaROF - ROF-related regularisation parameter
+* 3. lambdaLLT - LLT-related regularisation parameter
+* 4. tau - time-marching step 
+* 5. iter - iterations number (for both models)
+*
+* Output:
+* Filtered/regularised image
+*
+* References: 
+* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.
+* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+*/
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iterationsNumb, dimX, dimY, dimZ;
+    const int  *dim_array;
+    float *Input, *Output=NULL, lambdaROF, lambdaLLT, tau;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    if ((nrhs < 3) || (nrhs > 5)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter (ROF), Regularisation parameter (LTT), iterations number, time-marching parameter");
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    lambdaROF =  (float) mxGetScalar(prhs[1]); /* ROF regularization parameter */
+    lambdaLLT =  (float) mxGetScalar(prhs[2]); /* ROF regularization parameter */    
+    iterationsNumb = 250;
+    tau =  0.0025;
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }   
+    if ((nrhs == 4) || (nrhs == 5)) iterationsNumb =  (int) mxGetScalar(prhs[3]); /* iterations number */    
+    if (nrhs == 5) tau =  (float) mxGetScalar(prhs[4]); /* marching step parameter */  
+        
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));                        
+    }    
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));   
+  
+    LLT_ROF_CPU_main(Input, Output, lambdaROF, lambdaLLT, iterationsNumb, tau, dimX, dimY, dimZ);    
+}
\ No newline at end of file
diff --git a/Wrappers/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp
new file mode 100644
index 0000000..37563b0
--- /dev/null
+++ b/Wrappers/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp
@@ -0,0 +1,81 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "LLT_ROF_GPU_core.h"
+
+/* CUDA implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty.
+* 
+* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. 
+* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase 
+* lambdaLLT starting with smaller values. 
+*
+* Input Parameters:
+* 1. U0 - original noise image/volume
+* 2. lambdaROF - ROF-related regularisation parameter
+* 3. lambdaLLT - LLT-related regularisation parameter
+* 4. tau - time-marching step 
+* 5. iter - iterations number (for both models)
+*
+* Output:
+* Filtered/regularised image
+*
+* References: 
+* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.
+* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+*/
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iterationsNumb, dimX, dimY, dimZ;
+    const int  *dim_array;
+    float *Input, *Output=NULL, lambdaROF, lambdaLLT, tau;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    if ((nrhs < 3) || (nrhs > 5)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter (ROF), Regularisation parameter (LTT), iterations number, time-marching parameter");
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    lambdaROF =  (float) mxGetScalar(prhs[1]); /* ROF regularization parameter */
+    lambdaLLT =  (float) mxGetScalar(prhs[2]); /* ROF regularization parameter */    
+    iterationsNumb = 250;
+    tau =  0.0025;
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }   
+    if ((nrhs == 4) || (nrhs == 5)) iterationsNumb =  (int) mxGetScalar(prhs[3]); /* iterations number */    
+    if (nrhs == 5) tau =  (float) mxGetScalar(prhs[4]); /* marching step parameter */  
+        
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));                        
+    }    
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));   
+  
+    LLT_ROF_GPU_main(Input, Output, lambdaROF, lambdaLLT, iterationsNumb, tau, dimX, dimY, dimZ);    
+}
\ No newline at end of file
diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index 0e435a6..52c7974 100644
--- a/Wrappers/Python/ccpi/filters/regularisers.py
+++ b/Wrappers/Python/ccpi/filters/regularisers.py
@@ -2,8 +2,8 @@
 script which assigns a proper device core function based on a flag ('cpu' or 'gpu')
 """
 
-from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU, TGV_CPU
-from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU, TGV_GPU
+from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU
+from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU, TGV_GPU, LLT_ROF_GPU
 from ccpi.filters.cpu_regularisers import NDF_INPAINT_CPU, NVM_INPAINT_CPU
 
 def ROF_TV(inputData, regularisation_parameter, iterations,
@@ -147,7 +147,15 @@ def TGV(inputData, regularisation_parameter, alpha1, alpha0, iterations,
     else:
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
-                         
+def LLT_ROF(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations,
+                     time_marching_parameter, device='cpu'):
+    if device == 'cpu':
+        return LLT_ROF_CPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+    elif device == 'gpu':
+        return LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+    else:
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
 def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations,
                      time_marching_parameter, penalty_type):
         return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, 
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index 5c20244..b94f11c 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, FGP_dTV, TNV, NDF, DIFF4th
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, TNV, NDF, DIFF4th
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -256,6 +256,54 @@ imgplot = plt.imshow(tgv_cpu, cmap="gray")
 plt.title('{}'.format('CPU results'))
 
 #%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("______________LLT- ROF (2D)________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : u0,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.01, \
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+        
+print ("#############LLT- ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_cpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+
+rms = rmse(Im, lltrof_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+
+
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("________________NDF (2D)___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers3D.py b/Wrappers/Python/demos/demo_cpu_regularisers3D.py
index 8ee157e..9c28de1 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers3D.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers3D.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, FGP_dTV, NDF, DIFF4th
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, LLT_ROF, FGP_dTV, NDF, DIFF4th
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -85,7 +85,7 @@ print ("_______________ROF-TV (3D)_________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(1)
+fig = plt.figure()
 plt.suptitle('Performance of ROF-TV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy 15th slice of a volume')
@@ -120,13 +120,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(rof_cpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the CPU using ROF-TV'))
 
-
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________FGP-TV (3D)__________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(2)
+fig = plt.figure()
 plt.suptitle('Performance of FGP-TV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -170,12 +170,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(fgp_cpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the CPU using FGP-TV'))
 
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________SB-TV (3D)_________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(3)
+fig = plt.figure()
 plt.suptitle('Performance of SB-TV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -216,12 +217,58 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(sb_cpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the CPU using SB-TV'))
 
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________LLT-ROF (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : noisyVol,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.015, \
+        'number_of_iterations' :300 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+
+print ("#############LLT ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_cpu3D = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+
+rms = rmse(idealVol, lltrof_cpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using LLT-ROF'))
+
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("________________NDF (3D)___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(4)
+fig = plt.figure()
 plt.suptitle('Performance of NDF regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy volume')
@@ -262,13 +309,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(ndf_cpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the CPU using NDF iterations'))
 
-
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("___Anisotropic Diffusion 4th Order (2D)____")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(5)
+fig = plt.figure()
 plt.suptitle('Performance of Diff4th regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy volume')
@@ -307,13 +354,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(diff4th_cpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the CPU using DIFF4th iterations'))
 
-
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________FGP-dTV (3D)__________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(6)
+fig = plt.figure()
 plt.suptitle('Performance of FGP-dTV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
index 46b8ffc..e45dc40 100644
--- a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, FGP_dTV, NDF, DIFF4th
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, DIFF4th
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -352,8 +352,7 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(tgv_cpu, cmap="gray")
 plt.title('{}'.format('CPU results'))
 
-
-print ("##############SB TV GPU##################")
+print ("##############TGV GPU##################")
 start_time = timeit.default_timer()
 tgv_gpu = TGV(pars['input'], 
               pars['regularisation_parameter'],
@@ -392,6 +391,87 @@ else:
     print ("Arrays match")
 #%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________LLT-ROF bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of LLT-ROF regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : u0,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.01, \
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+        
+print ("#############LLT- ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_cpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+
+rms = rmse(Im, lltrof_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("#############LLT- ROF GPU####################")
+start_time = timeit.default_timer()
+lltrof_gpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+
+rms = rmse(Im, lltrof_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = LLT_ROF
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(lltrof_gpu))
+diff_im = abs(lltrof_cpu - lltrof_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________NDF bench___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py
index 792a019..de0cbde 100644
--- a/Wrappers/Python/demos/demo_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_gpu_regularisers.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, FGP_dTV, NDF, DIFF4th
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, DIFF4th
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -254,6 +254,53 @@ imgplot = plt.imshow(tgv_gpu, cmap="gray")
 plt.title('{}'.format('GPU results'))
 
 #%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("______________LLT- ROF (2D)________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : u0,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.01, \
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+        
+print ("#############LLT- ROF GPU####################")
+start_time = timeit.default_timer()
+lltrof_gpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+             
+             
+rms = rmse(Im, lltrof_gpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________NDF regulariser_____________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers3D.py b/Wrappers/Python/demos/demo_gpu_regularisers3D.py
index 13c4e7b..d5f9a39 100644
--- a/Wrappers/Python/demos/demo_gpu_regularisers3D.py
+++ b/Wrappers/Python/demos/demo_gpu_regularisers3D.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, FGP_dTV, NDF, DIFF4th
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, LLT_ROF, FGP_dTV, NDF, DIFF4th
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -86,12 +86,13 @@ for i in range (slices):
     noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im))
     idealVol[i,:,:] = Im
 
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________ROF-TV (3D)_________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(1)
+fig = plt.figure()
 plt.suptitle('Performance of ROF-TV regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy 15th slice of a volume')
@@ -125,13 +126,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
          verticalalignment='top', bbox=props)
 imgplot = plt.imshow(rof_gpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the GPU using ROF-TV'))
-
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________FGP-TV (3D)__________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(2)
+fig = plt.figure()
 plt.suptitle('Performance of FGP-TV regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -174,12 +175,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(fgp_gpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the GPU using FGP-TV'))
 
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________SB-TV (3D)__________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(3)
+fig = plt.figure()
 plt.suptitle('Performance of SB-TV regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -219,14 +221,58 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
          verticalalignment='top', bbox=props)
 imgplot = plt.imshow(sb_gpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the GPU using SB-TV'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________LLT-ROF (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : noisyVol,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.015, \
+        'number_of_iterations' :300 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+
+print ("#############LLT ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_gpu3D = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
 
+rms = rmse(idealVol, lltrof_gpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
 
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using LLT-ROF'))
+
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________NDF-TV (3D)_________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(4)
+fig = plt.figure()
 plt.suptitle('Performance of NDF regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -267,13 +313,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(ndf_gpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the GPU using NDF'))
 
-
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("___Anisotropic Diffusion 4th Order (3D)____")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(5)
+fig = plt.figure()
 plt.suptitle('Performance of DIFF4th regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -312,13 +358,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(diff4_gpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('GPU results'))
 
-
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________FGP-dTV (3D)________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(6)
+fig = plt.figure()
 plt.suptitle('Performance of FGP-dTV regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
diff --git a/Wrappers/Python/setup-regularisers.py.in b/Wrappers/Python/setup-regularisers.py.in
index 89ebaf9..7108683 100644
--- a/Wrappers/Python/setup-regularisers.py.in
+++ b/Wrappers/Python/setup-regularisers.py.in
@@ -39,6 +39,7 @@ extra_include_dirs += [os.path.join(".." , ".." , "Core"),
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_ROF" ) , 
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_SB" ) ,
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TGV" ) ,
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "LLTROF" ) ,
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "NDF" ) ,
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "dTV_FGP" ) , 
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "DIFF4th" ) , 
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index cf81bec..bf9c861 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -21,6 +21,7 @@ cimport numpy as np
 cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
 cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
 cdef extern float SB_TV_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ);
+cdef extern float LLT_ROF_CPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
 cdef extern float TGV_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY);
 cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ);
 cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
@@ -222,7 +223,51 @@ def TGV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        LipshitzConst,
                        dims[1],dims[0])                           
     return outputData
+
+#***************************************************************#
+#******************* ROF - LLT regularisation ******************#
+#***************************************************************#
+def LLT_ROF_CPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter):
+    if inputData.ndim == 2:
+        return LLT_ROF_2D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+    elif inputData.ndim == 3:
+        return LLT_ROF_3D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+
+def LLT_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+                         
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+                   
+    #/* Run ROF-LLT iterations for 2D data */
+    LLT_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[1],dims[0],1)
+    return outputData
+
+def LLT_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+						 
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
     
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
+           
+    #/* Run ROF-LLT iterations for 3D data */
+    LLT_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[2], dims[1], dims[0])
+    return outputData 
+
 #****************************************************************#
 #**************Directional Total-variation FGP ******************#
 #****************************************************************#
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index 4a202d7..82d3e01 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -22,6 +22,7 @@ cdef extern void TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, i
 cdef extern void TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z);
 cdef extern void TV_SB_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int printM, int N, int M, int Z);
 cdef extern void TGV_GPU_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY);
+cdef extern void LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int N, int M, int Z);
 cdef extern void NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int N, int M, int Z);
 cdef extern void dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z);
 cdef extern void Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z);
@@ -87,6 +88,12 @@ def TV_SB_GPU(inputData,
                      tolerance_param,
                      methodTV,
                      printM)
+# LLT-ROF model
+def LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter):
+    if inputData.ndim == 2:
+        return LLT_ROF_GPU2D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+    elif inputData.ndim == 3:
+        return LLT_ROF_GPU3D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
 # Total Generilised Variation (TGV)
 def TGV_GPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst):
     if inputData.ndim == 2:
@@ -324,6 +331,46 @@ def SBTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     return outputData 
 
 #***************************************************************#
+#************************ LLT-ROF model ************************#
+#***************************************************************#
+#************Joint LLT-ROF model for higher order **************#
+def LLT_ROF_GPU2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+    
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1]], dtype='float32')
+          
+    # Running CUDA code here    
+    LLT_ROF_GPU_main(&inputData[0,0], &outputData[0,0],regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[1],dims[0],1);
+    return outputData
+    
+def LLT_ROF_GPU3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+    
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+          
+    # Running CUDA code here    
+    LLT_ROF_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[2], dims[1], dims[0]);
+    return outputData 
+
+
+#***************************************************************#
 #***************** Total Generalised Variation *****************#
 #***************************************************************#
 def TGV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,