44 files changed, 262 insertions, 862 deletions
diff --git a/CMakeLists.txt b/CMakeLists.txt
index e39dacd..6d931ee 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -14,12 +14,12 @@
 
 cmake_minimum_required (VERSION 3.0)
 
-project(FISTA)
+project(RGL)
 #https://stackoverflow.com/questions/13298504/using-cmake-with-setup-py
 
 # The version number.
-set (FISTA_VERSION_MAJOR 1)
-set (FISTA_VERSION_MINOR 0)
+set (RGL_VERSION_MAJOR 1)
+set (RGL_VERSION_MINOR 0)
 
 set (CIL_VERSION_MAJOR 0)
 set (CIL_VERSION_MINOR 9)
diff --git a/Core/CCPiDefines.h b/Core/CCPiDefines.h
index c6ddc7d..d3038f9 100644
--- a/Core/CCPiDefines.h
+++ b/Core/CCPiDefines.h
@@ -3,7 +3,7 @@ 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 Srikanth Nagella, Edoardo Pasca
+Copyright 2017 Srikanth Nagella, Edoardo Pasca, Daniil Kazantsev
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
diff --git a/Core/CMakeLists.txt b/Core/CMakeLists.txt
index f53c538..3bc5ceb 100644
--- a/Core/CMakeLists.txt
+++ b/Core/CMakeLists.txt
@@ -1,7 +1,7 @@
 #   Copyright 2018 Edoardo Pasca
 cmake_minimum_required (VERSION 3.0)
 
-project(RegularizerLibrary)
+project(RGL)
 #https://stackoverflow.com/questions/13298504/using-cmake-with-setup-py
 
 # The version number.
@@ -41,8 +41,8 @@ if (OPENMP_FOUND)
     set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS} ${OpenMP_CXX_FLAGS}")
 endif()
 
-## Build the regularizers package as a library
-message("Creating Regularizers as shared library")
+## Build the regularisers package as a library
+message("Creating Regularisers as a shared library")
 
 message("CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS}")
 
@@ -76,26 +76,26 @@ elseif(UNIX)
 endif()
 message("CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS}")
 
-## Build the regularizers package as a library
-message("Adding regularizers as shared library")
+## Build the regularisers package as a library
+message("Adding regularisers as a shared library")
 
 #set(CMAKE_C_COMPILER /apps/pgi/linux86-64/17.4/bin/pgcc)
 #set(CMAKE_C_FLAGS "-acc -Minfo -ta=tesla:cc20 -openmp")
 #set(CMAKE_C_FLAGS "-acc -Minfo -ta=multicore -openmp -fPIC")
 add_library(cilreg SHARED
-	    ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/FGP_TV_core.c
-	    #${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/LLT_model_core.c
-	    #${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/PatchBased_Regul_core.c
-	    #${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/SplitBregman_TV_core.c
-	    #${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/TGV_PD_core.c
-        ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/ROF_TV_core.c
-	    ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/utils.c
+	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/FGP_TV_core.c
+	    #${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/SplitBregman_TV_core.c
+	    #${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/TGV_PD_core.c
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/ROF_TV_core.c
+	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/utils.c
 	    )
 target_link_libraries(cilreg ${EXTRA_LIBRARIES} )
 include_directories(cilreg PUBLIC 
                       ${LIBRARY_INC}/include 
 					  ${CMAKE_CURRENT_SOURCE_DIR}
-		              ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/  )
+		              ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/  )
 #GENERATE_EXPORT_HEADER( cilreg
 #             BASE_NAME cilreg
 #             EXPORT_MACRO_NAME CCPiCore_EXPORTS
@@ -120,15 +120,15 @@ message ("I'd install into ${CMAKE_INSTALL_PREFIX} lib bin")
 	)
 endif()
 
-# GPU Regularizers
+# GPU Regularisers
 
 find_package(CUDA)
 if (CUDA_FOUND)
   set(CUDA_NVCC_FLAGS "-Xcompiler -fPIC -shared -D_FORCE_INLINES")
   message("CUDA FLAGS ${CUDA_NVCC_FLAGS}")
   CUDA_ADD_LIBRARY(cilregcuda SHARED
-	${CMAKE_CURRENT_SOURCE_DIR}/regularizers_GPU/TV_ROF_GPU_core.cu
-	${CMAKE_CURRENT_SOURCE_DIR}/regularizers_GPU/TV_FGP_GPU_core.cu
+	${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/TV_ROF_GPU_core.cu
+	${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/TV_FGP_GPU_core.cu
   )
   if (UNIX)
     message ("I'd install into ${CMAKE_INSTALL_PREFIX}/lib")
@@ -149,6 +149,6 @@ else()
 endif()
 
 
-#add_executable(regularizer_test ${CMAKE_CURRENT_SOURCE_DIR}/test/test_regularizer.cpp)
+#add_executable(regulariser_test ${CMAKE_CURRENT_SOURCE_DIR}/test/test_regulariser.cpp)
 
-#target_link_libraries (regularizer_test LINK_PUBLIC regularizers_lib)
+#target_link_libraries (regulariser_test LINK_PUBLIC regularisers_lib)
diff --git a/Core/regularizers_CPU/FGP_TV_core.c b/Core/regularisers_CPU/FGP_TV_core.c
index 462c3f7..15bb45c 100644
--- a/Core/regularizers_CPU/FGP_TV_core.c
+++ b/Core/regularisers_CPU/FGP_TV_core.c
@@ -255,7 +255,7 @@ float Grad_func3D(float *P1, float *P2, float *P3, float *D, float *R1, float *R
 {
     float val1, val2, val3, multip;
     int i,j,k, index;
-    multip = (1.0f/(8.0f*lambda));
+    multip = (1.0f/(26.0f*lambda));
 #pragma omp parallel for shared(P1,P2,P3,D,R1,R2,R3,multip) private(index,i,j,k,val1,val2,val3)
     for(i=0; i<dimX; i++) {
         for(j=0; j<dimY; j++) {
diff --git a/Core/regularizers_CPU/FGP_TV_core.h b/Core/regularisers_CPU/FGP_TV_core.h
index 37e32f7..37e32f7 100644
--- a/Core/regularizers_CPU/FGP_TV_core.h
+++ b/Core/regularisers_CPU/FGP_TV_core.h
diff --git a/Core/regularizers_CPU/ROF_TV_core.c b/Core/regularisers_CPU/ROF_TV_core.c
index 9ffb905..9ffb905 100644
--- a/Core/regularizers_CPU/ROF_TV_core.c
+++ b/Core/regularisers_CPU/ROF_TV_core.c
diff --git a/Core/regularizers_CPU/ROF_TV_core.h b/Core/regularisers_CPU/ROF_TV_core.h
index 14daf58..14daf58 100644
--- a/Core/regularizers_CPU/ROF_TV_core.h
+++ b/Core/regularisers_CPU/ROF_TV_core.h
diff --git a/Core/regularizers_CPU/utils.c b/Core/regularisers_CPU/utils.c
index 0c02c45..0c02c45 100644
--- a/Core/regularizers_CPU/utils.c
+++ b/Core/regularisers_CPU/utils.c
diff --git a/Core/regularizers_CPU/utils.h b/Core/regularisers_CPU/utils.h
index bd76bf0..bd76bf0 100644
--- a/Core/regularizers_CPU/utils.h
+++ b/Core/regularisers_CPU/utils.h
diff --git a/Core/regularizers_GPU/TV_FGP_GPU_core.cu b/Core/regularisers_GPU/TV_FGP_GPU_core.cu
index 61097fb..314a367 100755
--- a/Core/regularizers_GPU/TV_FGP_GPU_core.cu
+++ b/Core/regularisers_GPU/TV_FGP_GPU_core.cu
@@ -493,7 +493,7 @@ extern "C" void TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, in
             cudaMemset(R2, 0, ImSize*sizeof(float));
             cudaMemset(R3, 0, ImSize*sizeof(float));
             /********************** Run CUDA 3D kernel here ********************/    
-            multip = (1.0f/(8.0f*lambdaPar));
+            multip = (1.0f/(26.0f*lambdaPar));
     
             /* The main kernel */
         for (i = 0; i < iter; i++) {
diff --git a/Core/regularizers_GPU/TV_FGP_GPU_core.h b/Core/regularisers_GPU/TV_FGP_GPU_core.h
index 107d243..107d243 100755
--- a/Core/regularizers_GPU/TV_FGP_GPU_core.h
+++ b/Core/regularisers_GPU/TV_FGP_GPU_core.h
diff --git a/Core/regularizers_GPU/TV_ROF_GPU_core.cu b/Core/regularisers_GPU/TV_ROF_GPU_core.cu
index 1a54d02..1a54d02 100755
--- a/Core/regularizers_GPU/TV_ROF_GPU_core.cu
+++ b/Core/regularisers_GPU/TV_ROF_GPU_core.cu
diff --git a/Core/regularizers_GPU/TV_ROF_GPU_core.h b/Core/regularisers_GPU/TV_ROF_GPU_core.h
index d772aba..d772aba 100755
--- a/Core/regularizers_GPU/TV_ROF_GPU_core.h
+++ b/Core/regularisers_GPU/TV_ROF_GPU_core.h
diff --git a/Readme.md b/Readme.md
index 3ec20dc..4931ee4 100644
--- a/Readme.md
+++ b/Readme.md
@@ -1,7 +1,7 @@
-# CCPi-Regularization Toolkit (CCPi-RGL)
+# CCPi-Regularisation Toolkit (CCPi-RGL)
 
-**Iterative image reconstruction (IIR) methods normally require regularization to stabilize the convergence and make the reconstruction problem more well-posed. 
-CCPi-RGL software consist of 2D/3D regularization modules for single-channel and multi-channel reconstruction problems. The modules especially suited for IIR, however,
+**Iterative image reconstruction (IIR) methods normally require regularisation to stabilise the convergence and make the reconstruction problem more well-posed. 
+CCPi-RGL software consist of 2D/3D regularisation modules for single-channel and multi-channel reconstruction problems. The modules especially suited for IIR, however,
 can also be used as image denoising iterative filters. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.** 
 
 ## Prerequisites: 
@@ -24,13 +24,13 @@ can also be used as image denoising iterative filters. The core modules are writ
 ### Python (conda-build)
 ```
 	export CIL_VERSION=0.9.2
-	conda build recipes/regularizers --numpy 1.12 --python 3.5 
-	conda install cil_regularizer=0.9.2 --use-local --force
+	conda build recipes/regularisers --numpy 1.12 --python 3.5 
+	conda install cil_regulariser=0.9.2 --use-local --force
 	cd Wrappers/Python
 	conda build conda-recipe --numpy 1.12 --python 3.5 
-	conda install ccpi-regularizer=0.9.2 --use-local --force
+	conda install ccpi-regulariser=0.9.2 --use-local --force
 	cd test/
-	python test_cpu_vs_gpu_regularizers.py
+	python test_cpu_vs_gpu_regularisers.py
 ```
 ### Matlab
 ```
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
new file mode 100644
index 0000000..f5c3ad1
--- /dev/null
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -0,0 +1,44 @@
+% Volume (3D) denoising demo using CCPi-RGL
+
+addpath('../mex_compile/installed');
+addpath('../../../data/');
+
+N = 256; 
+slices = 30;
+vol3D = zeros(N,N,slices, 'single');
+Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+for i = 1:slices
+vol3D(:,:,i) = Im + .05*randn(size(Im)); 
+end
+vol3D(vol3D < 0) = 0;
+figure; imshow(vol3D(:,:,15), [0 1]); title('Noisy image');
+
+%%
+fprintf('Denoise using ROF-TV model (CPU) \n');
+lambda_rof = 0.03; % regularisation parameter
+tau_rof = 0.0025; % time-marching constant 
+iter_rof = 1000; % number of ROF iterations
+tic; u_rof = ROF_TV(single(vol3D), lambda_rof, iter_rof, tau_rof); toc; 
+figure; imshow(u_rof(:,:,15), [0 1]); title('ROF-TV denoised volume (CPU)');
+%%
+% fprintf('Denoise using ROF-TV model (GPU) \n');
+% lambda_rof = 0.03; % regularisation parameter
+% tau_rof = 0.0025; % time-marching constant 
+% iter_rof = 1000; % number of ROF iterations
+% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_rof, iter_rof, tau_rof); toc;
+% figure; imshow(u_rofG(:,:,15), [0 1]); title('ROF-TV denoised volume (GPU)');
+%%
+fprintf('Denoise using FGP-TV model (CPU) \n');
+lambda_fgp = 0.03; % regularisation parameter
+iter_fgp = 500; % number of FGP iterations
+epsil_tol =  1.0e-05; % tolerance
+tic; u_fgp = FGP_TV(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; 
+figure; imshow(u_fgp(:,:,15), [0 1]); title('FGP-TV denoised volume (CPU)');
+%%
+% fprintf('Denoise using FGP-TV model (GPU) \n');
+% lambda_fgp = 0.03; % regularisation parameter
+% iter_fgp = 500; % number of FGP iterations
+% epsil_tol =  1.0e-05; % tolerance
+% tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; 
+% figure; imshow(u_fgpG(:,:,15), [0 1]); title('FGP-TV denoised volume (GPU)');
+%%
+\ No newline at end of file
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index 7258e5e..ab4e95d 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -9,28 +9,28 @@ figure; imshow(u0, [0 1]); title('Noisy image');
 
 %%
 fprintf('Denoise using ROF-TV model (CPU) \n');
-lambda_rof = 0.03; % regularization parameter
+lambda_rof = 0.03; % regularisation parameter
 tau_rof = 0.0025; % time-marching constant 
 iter_rof = 2000; % number of ROF iterations
 tic; u_rof = ROF_TV(single(u0), lambda_rof, iter_rof, tau_rof); toc; 
 figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)');
 %%
 % fprintf('Denoise using ROF-TV model (GPU) \n');
-% lambda_rof = 0.03; % regularization parameter
+% lambda_rof = 0.03; % regularisation parameter
 % tau_rof = 0.0025; % time-marching constant 
 % iter_rof = 2000; % number of ROF iterations
 % tic; u_rofG = ROF_TV_GPU(single(u0), lambda_rof, iter_rof, tau_rof); toc;
 % figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)');
 %%
 fprintf('Denoise using FGP-TV model (CPU) \n');
-lambda_fgp = 0.03; % regularization parameter
+lambda_fgp = 0.03; % regularisation parameter
 iter_fgp = 1000; % number of FGP iterations
 epsil_tol =  1.0e-05; % tolerance
 tic; u_fgp = FGP_TV(single(u0), lambda_fgp, iter_fgp, epsil_tol); toc; 
 figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)');
 %%
 % fprintf('Denoise using FGP-TV model (GPU) \n');
-% lambda_fgp = 0.03; % regularization parameter
+% lambda_fgp = 0.03; % regularisation parameter
 % iter_fgp = 1000; % number of FGP iterations
 % epsil_tol =  1.0e-05; % tolerance
 % tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_fgp, iter_fgp, epsil_tol); toc; 
diff --git a/Wrappers/Matlab/mex_compile/compileCPU_mex.m b/Wrappers/Matlab/mex_compile/compileCPU_mex.m
index fcee53a..8da81ad 100644
--- a/Wrappers/Matlab/mex_compile/compileCPU_mex.m
+++ b/Wrappers/Matlab/mex_compile/compileCPU_mex.m
@@ -1,10 +1,10 @@
 % execute this mex file in Matlab once
-copyfile ../../../Core/regularizers_CPU/ regularizers_CPU/
-copyfile ../../../Core/CCPiDefines.h regularizers_CPU/
+copyfile ../../../Core/regularisers_CPU/ regularisers_CPU/
+copyfile ../../../Core/CCPiDefines.h regularisers_CPU/
 
-cd regularizers_CPU/
+cd regularisers_CPU/
 
-fprintf('%s \n', 'Compiling CPU regularizers...');
+fprintf('%s \n', 'Compiling CPU regularisers...');
 mex ROF_TV.c ROF_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile ROF_TV.mex* ../installed/
 
diff --git a/Wrappers/Matlab/mex_compile/compileGPU_mex.m b/Wrappers/Matlab/mex_compile/compileGPU_mex.m
index df29a3e..45236fa 100644
--- a/Wrappers/Matlab/mex_compile/compileGPU_mex.m
+++ b/Wrappers/Matlab/mex_compile/compileGPU_mex.m
@@ -9,12 +9,12 @@
 
 % tested on Ubuntu 16.04/MATLAB 2016b
 
-copyfile ../../../Core/regularizers_GPU/ regularizers_GPU/
-copyfile ../../../Core/CCPiDefines.h regularizers_GPU/
+copyfile ../../../Core/regularisers_GPU/ regularisers_GPU/
+copyfile ../../../Core/CCPiDefines.h regularisers_GPU/
 
-cd regularizers_GPU/
+cd regularisers_GPU/
 
-fprintf('%s \n', 'Compiling GPU regularizers (CUDA)...');
+fprintf('%s \n', 'Compiling GPU regularisers (CUDA)...');
 !/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* ../installed/
diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/FGP_TV.c
index ba06cc7..ba06cc7 100644
--- a/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV.c
+++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/FGP_TV.c
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/FGP_TV.c~ b/Wrappers/Matlab/mex_compile/regularisers_CPU/FGP_TV.c~
new file mode 100644
index 0000000..30d61cd
--- /dev/null
+++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/FGP_TV.c~
@@ -0,0 +1,91 @@
+/*
+ * 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 "FGP_TV_core.h"
+
+/* C-OMP implementation of FGP-TV [1] denoising/regularization model (2D/3D case)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume
+ * 2. lambdaPar - regularization parameter
+ * 3. Number of iterations
+ * 4. eplsilon: tolerance constant
+ * 5. TV-type: methodTV - 'iso' (0) or 'l1' (1)
+ * 6. nonneg: 'nonnegativity (0 is OFF by default)
+ * 7. print information: 0 (off) or 1 (on)
+ *
+ * Output:
+ * [1] Filtered/regularized image
+ *
+ * This function is based on the Matlab's code and paper by
+ * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
+ */
+
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter, dimX, dimY, dimZ, methTV, printswitch;
+    const int  *dim_array;
+    float *Input, *Output, lambda, epsil;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), print switch");
+    
+    Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    iter = 300; /* default iterations number */
+    epsil = 0.0001; /* default tolerance constant */
+    methTV = 0;  /* default isotropic TV penalty */
+    printswitch = 0; /*default print is switched off - 0 */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6))  epsil =  (float) mxGetScalar(prhs[3]); /* tolerance constant */
+    if ((nrhs == 5) || (nrhs == 6))  {
+        char *penalty_type;
+        penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
+        if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
+        if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
+        mxFree(penalty_type);
+    }
+    if (nrhs == 6)  {
+        printswitch = (int) mxGetScalar(prhs[5]);
+        if ((printswitch != 0) || (printswitch != 1)) {mexErrMsgTxt("Print can be enabled by choosing 1 or off - 0"); }
+    }
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        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));
+    
+    
+    TV_FGP_CPU_main(Input, Output, lambda, iter, epsil, methTV, nonneg, printswitch, dimX, dimY, dimZ)
+}
diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/ROF_TV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
index 6b9e1ea..6b9e1ea 100644
--- a/Wrappers/Matlab/mex_compile/regularizers_CPU/ROF_TV.c
+++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
diff --git a/Wrappers/Matlab/mex_compile/regularizers_GPU/FGP_TV_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp
index 9ed9ae0..9ed9ae0 100644
--- a/Wrappers/Matlab/mex_compile/regularizers_GPU/FGP_TV_GPU.cpp
+++ b/Wrappers/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp
diff --git a/Wrappers/Matlab/mex_compile/regularizers_GPU/ROF_TV_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
index 7bbe3af..7bbe3af 100644
--- a/Wrappers/Matlab/mex_compile/regularizers_GPU/ROF_TV_GPU.cpp
+++ b/Wrappers/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
diff --git a/Wrappers/Python/CMakeLists.txt b/Wrappers/Python/CMakeLists.txt
index eb3bac7..fb00706 100644
--- a/Wrappers/Python/CMakeLists.txt
+++ b/Wrappers/Python/CMakeLists.txt
@@ -1,7 +1,7 @@
 #   Copyright 2018 Edoardo Pasca
 cmake_minimum_required (VERSION 3.0)
 
-project(RegularizerPython)
+project(regulariserPython)
 #https://stackoverflow.com/questions/13298504/using-cmake-with-setup-py
 
 # The version number.
@@ -25,8 +25,8 @@ if (PYTHONINTERP_FOUND)
 endif()
 
 	
-## Build the regularizers package as a library
-message("Creating Regularizers as shared library")
+## Build the regularisers package as a library
+message("Creating Regularisers as shared library")
 
 message("CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS}")
 
@@ -52,7 +52,7 @@ elseif(UNIX)
 		)
 endif()
 
-# GPU Regularizers
+# GPU regularisers
 
 find_package(CUDA)
 if (CUDA_FOUND)
@@ -60,12 +60,12 @@ if (CUDA_FOUND)
   set (SETUP_GPU_WRAPPERS "extra_libraries += ['cilregcuda']\n\
 setup( \n\
     name='ccpi', \n\
- 	description='CCPi Core Imaging Library - Image Regularizers GPU',\n\
+ 	description='CCPi Core Imaging Library - Image regularisers GPU',\n\
  	version=cil_version,\n\
     cmdclass = {'build_ext': build_ext},\n\
-    ext_modules = [Extension('ccpi.filters.gpu_regularizers',\n\
+    ext_modules = [Extension('ccpi.filters.gpu_regularisers',\n\
                               sources=[ \n\
-                                      os.path.join('.' , 'src', 'gpu_regularizers.pyx' ),\n\
+                                      os.path.join('.' , 'src', 'gpu_regularisers.pyx' ),\n\
                                         ],\n\
                              include_dirs=extra_include_dirs, \n\
  							 library_dirs=extra_library_dirs, \n\
@@ -80,9 +80,9 @@ else()
   set(SETUP_GPU_WRAPPERS "#CUDA NOT FOUND")
 endif()
 
-configure_file("setup-regularizers.py.in" "setup-regularizers.py")
+configure_file("setup-regularisers.py.in" "setup-regularisers.py")
 
 
-#add_executable(regularizer_test ${CMAKE_CURRENT_SOURCE_DIR}/test/test_regularizer.cpp)
+#add_executable(regulariser_test ${CMAKE_CURRENT_SOURCE_DIR}/test/test_regulariser.cpp)
 
-#target_link_libraries (regularizer_test LINK_PUBLIC regularizers_lib)
+#target_link_libraries (regulariser_test LINK_PUBLIC regularisers_lib)
diff --git a/Wrappers/Python/ccpi/filters/regularizers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index d6dfa8c..039daab 100644
--- a/Wrappers/Python/ccpi/filters/regularizers.py
+++ b/Wrappers/Python/ccpi/filters/regularisers.py
@@ -2,30 +2,30 @@
 script which assigns a proper device core function based on a flag ('cpu' or 'gpu')
 """
 
-from ccpi.filters.cpu_regularizers_cython import TV_ROF_CPU, TV_FGP_CPU
-from ccpi.filters.gpu_regularizers import TV_ROF_GPU, TV_FGP_GPU
+from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU
+from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU
 
-def ROF_TV(inputData, regularization_parameter, iterations,
+def ROF_TV(inputData, regularisation_parameter, iterations,
                      time_marching_parameter,device='cpu'):
     if device == 'cpu':
         return TV_ROF_CPU(inputData,
-                     regularization_parameter,
+                     regularisation_parameter,
                      iterations, 
                      time_marching_parameter)
     elif device == 'gpu':
         return TV_ROF_GPU(inputData,
-                     regularization_parameter,
+                     regularisation_parameter,
                      iterations, 
                      time_marching_parameter)
     else:
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
 
-def FGP_TV(inputData, regularization_parameter,iterations,
+def FGP_TV(inputData, regularisation_parameter,iterations,
                      tolerance_param, methodTV, nonneg, printM, device='cpu'):
     if device == 'cpu':
         return TV_FGP_CPU(inputData,
-                     regularization_parameter,
+                     regularisation_parameter,
                      iterations, 
                      tolerance_param,
                      methodTV,
@@ -33,7 +33,7 @@ def FGP_TV(inputData, regularization_parameter,iterations,
                      printM)
     elif device == 'gpu':
         return TV_FGP_GPU(inputData,
-                     regularization_parameter,
+                     regularisation_parameter,
                      iterations, 
                      tolerance_param,
                      methodTV,
@@ -41,4 +41,4 @@ def FGP_TV(inputData, regularization_parameter,iterations,
                      printM)
     else:
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
-                         .format(device))
-\ No newline at end of file
+                         .format(device))
diff --git a/Wrappers/Python/conda-recipe/bld.bat b/Wrappers/Python/conda-recipe/bld.bat
index 850905c..e47f8d9 100644
--- a/Wrappers/Python/conda-recipe/bld.bat
+++ b/Wrappers/Python/conda-recipe/bld.bat
@@ -11,7 +11,7 @@ cd %SRC_DIR%\ccpi\Python
 :: issue cmake to create setup.py
 cmake . 
 
-%PYTHON% setup-regularizers.py build_ext
+%PYTHON% setup-regularisers.py build_ext
 if errorlevel 1 exit 1
-%PYTHON% setup-regularizers.py install
+%PYTHON% setup-regularisers.py install
 if errorlevel 1 exit 1
diff --git a/Wrappers/Python/conda-recipe/build.sh b/Wrappers/Python/conda-recipe/build.sh
index 9ea4161..8b05663 100644
--- a/Wrappers/Python/conda-recipe/build.sh
+++ b/Wrappers/Python/conda-recipe/build.sh
@@ -13,7 +13,7 @@ echo "$SRC_DIR/ccpi/Python"
 
 cmake . 
 
-$PYTHON setup-regularizers.py build_ext
-$PYTHON setup-regularizers.py install
+$PYTHON setup-regularisers.py build_ext
+$PYTHON setup-regularisers.py install
 
 
diff --git a/Wrappers/Python/conda-recipe/meta.yaml b/Wrappers/Python/conda-recipe/meta.yaml
index f4cb471..5336d14 100644
--- a/Wrappers/Python/conda-recipe/meta.yaml
+++ b/Wrappers/Python/conda-recipe/meta.yaml
@@ -1,5 +1,5 @@
 package:
-  name: ccpi-regularizer
+  name: ccpi-regulariser
   version: {{ environ['CIL_VERSION'] }}
 
 
@@ -17,7 +17,7 @@ requirements:
     #- boost ==1.64.0
     #- boost-cpp ==1.64.0
     - cython
-    - cil_regularizer
+    - cil_regulariser
     - vc 14 # [win and py35] 
     - vc 9  # [win and py27]
     - cmake 
@@ -26,7 +26,7 @@ requirements:
     - python
     - numpy x.x
     #- boost ==1.64
-    - cil_regularizer
+    - cil_regulariser
     - vc 14 # [win and py35] 
     - vc 9  # [win and py27]
 
diff --git a/Wrappers/Python/demo/test_cpu_regularizers.py b/Wrappers/Python/demo/test_cpu_regularisers.py
index 76b9de7..4e4a2dd 100644
--- a/Wrappers/Python/demo/test_cpu_regularizers.py
+++ b/Wrappers/Python/demo/test_cpu_regularisers.py
@@ -5,24 +5,13 @@ Created on Fri Aug  4 11:10:05 2017
 @author: ofn77899
 """
 
-
 import matplotlib.pyplot as plt
 import numpy as np
 import os    
 from enum import Enum
 import timeit
-from ccpi.filters.cpu_regularizers_boost import SplitBregman_TV, LLT_model, PatchBased_Regul, TGV_PD
-from ccpi.filters.regularizers import ROF_TV, FGP_TV
+from ccpi.filters.regularisers import ROF_TV, FGP_TV
 ###############################################################################
-#https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
-#NRMSE a normalization of the root of the mean squared error
-#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum
-# intensity from the two images being compared, and respectively the same for
-# minval. RMSE is given by the square root of MSE: 
-# sqrt[(sum(A - B) ** 2) / |A|],
-# where |A| means the number of elements in A. By doing this, the maximum value
-# given by RMSE is maxval.
-
 def nrmse(im1, im2):
     a, b = im1.shape
     rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b))
@@ -51,17 +40,8 @@ def printParametersToString(pars):
 #  2D Regularizers
 #
 ###############################################################################
-#Example:
-# figure;
-# Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
 # assumes the script is launched from the test directory
 filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
-#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
-#filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
-#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
 
 Im = plt.imread(filename)                     
 Im = np.asarray(Im, dtype='float32')
@@ -86,48 +66,14 @@ imgplot = plt.imshow(u0,cmap="gray"
 
 reg_output = []
 ##############################################################################
-# Call regularizer
-
-####################### SplitBregman_TV #####################################
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
-start_time = timeit.default_timer()
-pars = {'algorithm' : SplitBregman_TV , \
-        'input' : u0,
-        'regularization_parameter':15. , \
-        'number_of_iterations' :40 ,\
-        'tolerance_constant':0.0001 , \
-        'TV_penalty': 0
-}
-
-out = SplitBregman_TV (pars['input'], pars['regularization_parameter'],
-                              pars['number_of_iterations'],
-                              pars['tolerance_constant'],
-                              pars['TV_penalty'])  
-splitbregman = out[0]
-rms = rmse(Im, splitbregman)
-pars['rmse'] = rms
-txtstr = printParametersToString(pars) 
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)   
-
-a=fig.add_subplot(2,4,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
-        verticalalignment='top', bbox=props)
-imgplot = plt.imshow(splitbregman,\
-                     cmap="gray"
-                     )
+# Call regularisers
 
 ###################### FGP_TV #########################################
 
 start_time = timeit.default_timer()
 pars = {'algorithm' : FGP_TV , \
         'input' : u0,\
-        'regularization_parameter':0.07, \
+        'regularisation_parameter':0.07, \
         'number_of_iterations' :300 ,\
         'tolerance_constant':0.00001,\
         'methodTV': 0 ,\
@@ -136,7 +82,7 @@ pars = {'algorithm' : FGP_TV , \
         }
 
 fgp = FGP_TV(pars['input'], 
-              pars['regularization_parameter'],
+              pars['regularisation_parameter'],
               pars['number_of_iterations'],
               pars['tolerance_constant'], 
               pars['methodTV'],
@@ -163,129 +109,18 @@ imgplot = plt.imshow(fgp, \
 a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
         verticalalignment='top', bbox=props)
 
-
-###################### LLT_model #########################################
-start_time = timeit.default_timer()
-
-pars = {'algorithm': LLT_model , \
-        'input' : u0,
-        'regularization_parameter': 5,\
-        'time_step':0.00035, \
-        'number_of_iterations' :350,\
-        'tolerance_constant':0.0001,\
-        'restrictive_Z_smoothing': 0
-}
-out = LLT_model(pars['input'], 
-                pars['regularization_parameter'],
-                pars['time_step'] , 
-                pars['number_of_iterations'],
-                pars['tolerance_constant'],
-                pars['restrictive_Z_smoothing'] )
-
-llt = out[0]
-rms = rmse(Im, out[0])
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(2,4,4)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(llt,\
-                     cmap="gray"
-                     )
-# ###################### PatchBased_Regul #########################################
-# # Quick 2D denoising example in Matlab:   
-# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# #   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
-
-start_time = timeit.default_timer()
-
-pars = {'algorithm': PatchBased_Regul , \
-        'input' : u0,
-        'regularization_parameter': 0.05,\
-        'searching_window_ratio':3, \
-        'similarity_window_ratio':1,\
-        'PB_filtering_parameter': 0.06
-}
-out = PatchBased_Regul(pars['input'], 
-                       pars['regularization_parameter'],
-                       pars['searching_window_ratio'] , 
-                       pars['similarity_window_ratio'] , 
-                       pars['PB_filtering_parameter'])
-pbr = out[0]
-rms = rmse(Im, out[0])
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-
-a=fig.add_subplot(2,4,5)
-
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
-        verticalalignment='top', bbox=props)
-imgplot = plt.imshow(pbr ,cmap="gray")
-
-
-# ###################### TGV_PD #########################################
-# # Quick 2D denoising example in Matlab:   
-# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# #   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
-
-start_time = timeit.default_timer()
-
-pars = {'algorithm': TGV_PD , \
-        'input' : u0,\
-        'regularization_parameter':0.07,\
-        'first_order_term': 1.3,\
-        'second_order_term': 1, \
-        'number_of_iterations': 550
-        }
-out = TGV_PD(pars['input'],
-             pars['regularization_parameter'],
-             pars['first_order_term'] , 
-             pars['second_order_term'] , 
-             pars['number_of_iterations'])
-tgv = out[0]
-rms = rmse(Im, out[0])
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(2,4,6)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(tgv, cmap="gray")
-
 # ###################### ROF_TV #########################################
 
 start_time = timeit.default_timer()
 
 pars = {'algorithm': ROF_TV , \
         'input' : u0,\
-        'regularization_parameter':0.07,\
+        'regularisation_parameter':0.07,\
         'marching_step': 0.0025,\
         'number_of_iterations': 300
         }
 rof = ROF_TV(pars['input'],
-			 pars['regularization_parameter'],
+			 pars['regularisation_parameter'],
              pars['number_of_iterations'],             
              pars['marching_step'], 'cpu')
 
diff --git a/Wrappers/Python/fista-recipe/bld.bat b/Wrappers/Python/fista-recipe/bld.bat
deleted file mode 100644
index 32c1bc6..0000000
--- a/Wrappers/Python/fista-recipe/bld.bat
+++ /dev/null
@@ -1,13 +0,0 @@
-IF NOT DEFINED CIL_VERSION (
-ECHO CIL_VERSION Not Defined.
-exit 1
-)
-
-mkdir "%SRC_DIR%\ccpifista"
-xcopy /e "%RECIPE_DIR%\.." "%SRC_DIR%\ccpifista"
-
-cd "%SRC_DIR%\ccpifista"
-::%PYTHON% setup-fista.py -q bdist_egg
-:: %PYTHON% setup.py install --single-version-externally-managed --record=record.txt
-%PYTHON% setup-fista.py install
-if errorlevel 1 exit 1
diff --git a/Wrappers/Python/fista-recipe/build.sh b/Wrappers/Python/fista-recipe/build.sh
deleted file mode 100644
index e3f3552..0000000
--- a/Wrappers/Python/fista-recipe/build.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-if [ -z "$CIL_VERSION" ]; then
-    echo "Need to set CIL_VERSION"
-    exit 1
-fi  
-mkdir "$SRC_DIR/ccpifista"
-cp -r "$RECIPE_DIR/.." "$SRC_DIR/ccpifista"
-
-cd $SRC_DIR/ccpifista
-
-$PYTHON setup-fista.py install
diff --git a/Wrappers/Python/fista-recipe/meta.yaml b/Wrappers/Python/fista-recipe/meta.yaml
deleted file mode 100644
index 4e5cba6..0000000
--- a/Wrappers/Python/fista-recipe/meta.yaml
+++ /dev/null
@@ -1,29 +0,0 @@
-package:
-  name: ccpi-fista
-  version: {{ environ['CIL_VERSION'] }}
-
-
-build:
-  preserve_egg_dir: False
-  script_env:
-    - CIL_VERSION   
-#  number: 0
-  
-requirements:
-  build:
-    - python
-    - numpy
-    - setuptools
- 
-  run:
-    - python
-    - numpy
-    #- astra-toolbox
-    - ccpi-regularizer
-
-
-	
-about:
-  home: http://www.ccpi.ac.uk
-  license:  Apache v.2.0 license
-  summary: 'CCPi Core Imaging Library (Viewer)'
diff --git a/Wrappers/Python/setup-fista.py b/Wrappers/Python/setup-fista.py
deleted file mode 100644
index c5c9f4d..0000000
--- a/Wrappers/Python/setup-fista.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from distutils.core import setup
-#from setuptools import setup, find_packages
-import os
-
-cil_version=os.environ['CIL_VERSION']
-if  cil_version == '':
-    print("Please set the environmental variable CIL_VERSION")
-    sys.exit(1)
-
-setup(
-    name="ccpi-fista",
-    version=cil_version,
-    packages=['ccpi','ccpi.reconstruction'],
-	install_requires=['numpy'],
-
-     zip_safe = False,
-
-    # metadata for upload to PyPI
-    author="Edoardo Pasca",
-    author_email="edo.paskino@gmail.com",
-    description='CCPi Core Imaging Library - FISTA Reconstructor module',
-    license="Apache v2.0",
-    keywords="tomography interative reconstruction",
-    url="http://www.ccpi.ac.uk",   # project home page, if any
-
-    # could also include long_description, download_url, classifiers, etc.
-)
diff --git a/Wrappers/Python/setup-regularizers.py.in b/Wrappers/Python/setup-regularisers.py.in
index 0811372..a1c1ab6 100644
--- a/Wrappers/Python/setup-regularizers.py.in
+++ b/Wrappers/Python/setup-regularisers.py.in
@@ -33,9 +33,9 @@ extra_link_args = []
 extra_libraries = ['cilreg']
 
 extra_include_dirs += [os.path.join(".." , ".." , "Core"),
-                       os.path.join(".." , ".." , "Core",  "regularizers_CPU"),
-                       os.path.join(".." , ".." , "Core",  "regularizers_GPU" , "TV_FGP" ) , 
-                       os.path.join(".." , ".." , "Core",  "regularizers_GPU" , "TV_ROF" ) , 
+                       os.path.join(".." , ".." , "Core",  "regularisers_CPU"),
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_FGP" ) , 
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_ROF" ) , 
 						   "."]
 
 if platform.system() == 'Windows':				   
@@ -46,11 +46,11 @@ else:
     
 setup(
     name='ccpi',
-	description='CCPi Core Imaging Library - Image Regularizers',
+	description='CCPi Core Imaging Library - Image regularisers',
 	version=cil_version,
     cmdclass = {'build_ext': build_ext},
-    ext_modules = [Extension("ccpi.filters.cpu_regularizers_cython",
-                             sources=[os.path.join("." , "src", "cpu_regularizers.pyx" ) ],
+    ext_modules = [Extension("ccpi.filters.cpu_regularisers_cython",
+                             sources=[os.path.join("." , "src", "cpu_regularisers.pyx" ) ],
                              include_dirs=extra_include_dirs, 
 							 library_dirs=extra_library_dirs, 
 							 extra_compile_args=extra_compile_args, 
diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index f993e54..248bad1 100644
--- a/Wrappers/Python/src/cpu_regularizers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -25,14 +25,14 @@ cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar,
 #****************************************************************#
 #********************** Total-variation ROF *********************#
 #****************************************************************#
-def TV_ROF_CPU(inputData, regularization_parameter, iterationsNumb, marching_step_parameter):
+def TV_ROF_CPU(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter):
     if inputData.ndim == 2:
-        return TV_ROF_2D(inputData, regularization_parameter, iterationsNumb, marching_step_parameter)
+        return TV_ROF_2D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter)
     elif inputData.ndim == 3:
-        return TV_ROF_3D(inputData, regularization_parameter, iterationsNumb, marching_step_parameter)
+        return TV_ROF_3D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter)
 
 def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
-                     float regularization_parameter,
+                     float regularisation_parameter,
                      int iterationsNumb,                     
                      float marching_step_parameter):
     cdef long dims[2]
@@ -43,13 +43,13 @@ def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
             np.zeros([dims[0],dims[1]], dtype='float32')
                    
     # Run ROF iterations for 2D data 
-    TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularization_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], 1)
+    TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], 1)
     
     return outputData
             
 def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
                      int iterationsNumb,
-                     float regularization_parameter,
+                     float regularisation_parameter,
                      float marching_step_parameter):
     cdef long dims[3]
     dims[0] = inputData.shape[0]
@@ -60,7 +60,7 @@ def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
             np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
            
     # Run ROF iterations for 3D data 
-    TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularization_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], dims[2])
+    TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], dims[2])
 
     return outputData
 
@@ -68,14 +68,14 @@ def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
 #********************** Total-variation FGP *********************#
 #****************************************************************#
 #******** Total-variation Fast-Gradient-Projection (FGP)*********#
-def TV_FGP_CPU(inputData, regularization_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM):
+def TV_FGP_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM):
     if inputData.ndim == 2:
-        return TV_FGP_2D(inputData, regularization_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM)
+        return TV_FGP_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM)
     elif inputData.ndim == 3:
-        return TV_FGP_3D(inputData, regularization_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM)
+        return TV_FGP_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM)
 
 def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
-                     float regularization_parameter,
+                     float regularisation_parameter,
                      int iterationsNumb, 
                      float tolerance_param,
                      int methodTV,
@@ -90,7 +90,7 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
             np.zeros([dims[0],dims[1]], dtype='float32')
                    
     #/* Run ROF iterations for 2D data */
-    TV_FGP_CPU_main(&inputData[0,0], &outputData[0,0], regularization_parameter, 
+    TV_FGP_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, 
                        iterationsNumb, 
                        tolerance_param,
                        methodTV,
@@ -101,7 +101,7 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     return outputData        
             
 def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
-                     float regularization_parameter,
+                     float regularisation_parameter,
                      int iterationsNumb, 
                      float tolerance_param,
                      int methodTV,
@@ -116,7 +116,7 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
             np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
            
     #/* Run ROF iterations for 3D data */
-    TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularization_parameter,
+    TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter,
                        iterationsNumb, 
                        tolerance_param,
                        methodTV,
diff --git a/Wrappers/Python/src/gpu_regularizers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index a44bd1d..7ebd011 100644
--- a/Wrappers/Python/src/gpu_regularizers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -23,23 +23,23 @@ cdef extern void TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, i
 
 # Total-variation Rudin-Osher-Fatemi (ROF)
 def TV_ROF_GPU(inputData,
-                     regularization_parameter,
+                     regularisation_parameter,
                      iterations, 
                      time_marching_parameter):
     if inputData.ndim == 2:
         return ROFTV2D(inputData, 
-                     regularization_parameter,
+                     regularisation_parameter,
                      iterations,
                      time_marching_parameter)
     elif inputData.ndim == 3:
         return ROFTV3D(inputData, 
-                     regularization_parameter,
+                     regularisation_parameter,
                      iterations, 
                      time_marching_parameter)
                      
 # Total-variation Fast-Gradient-Projection (FGP)
 def TV_FGP_GPU(inputData,
-                     regularization_parameter,
+                     regularisation_parameter,
                      iterations, 
                      tolerance_param,
                      methodTV,
@@ -47,7 +47,7 @@ def TV_FGP_GPU(inputData,
                      printM):
     if inputData.ndim == 2:
         return FGPTV2D(inputData,
-                     regularization_parameter,
+                     regularisation_parameter,
                      iterations, 
                      tolerance_param,
                      methodTV,
@@ -55,7 +55,7 @@ def TV_FGP_GPU(inputData,
                      printM)
     elif inputData.ndim == 3:
         return FGPTV3D(inputData,
-                     regularization_parameter,
+                     regularisation_parameter,
                      iterations, 
                      tolerance_param,
                      methodTV,
@@ -66,7 +66,7 @@ def TV_FGP_GPU(inputData,
 #********************** Total-variation ROF *********************#
 #****************************************************************#
 def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
-                     float regularization_parameter,
+                     float regularisation_parameter,
                      int iterations, 
                      float time_marching_parameter):
     
@@ -80,7 +80,7 @@ def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     # Running CUDA code here    
     TV_ROF_GPU_main(            
             &inputData[0,0], &outputData[0,0], 
-                       regularization_parameter,
+                       regularisation_parameter,
                        iterations , 
                        time_marching_parameter, 
                        dims[0], dims[1], 1);   
@@ -88,7 +88,7 @@ def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     return outputData
     
 def ROFTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
-                     float regularization_parameter,
+                     float regularisation_parameter,
                      int iterations, 
                      float time_marching_parameter):
     
@@ -103,7 +103,7 @@ def ROFTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     # Running CUDA code here    
     TV_ROF_GPU_main(            
             &inputData[0,0,0], &outputData[0,0,0], 
-                       regularization_parameter,
+                       regularisation_parameter,
                        iterations , 
                        time_marching_parameter, 
                        dims[0], dims[1], dims[2]);   
@@ -114,7 +114,7 @@ def ROFTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
 #****************************************************************#
 #******** Total-variation Fast-Gradient-Projection (FGP)*********#
 def FGPTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
-                     float regularization_parameter,
+                     float regularisation_parameter,
                      int iterations, 
                      float tolerance_param,
                      int methodTV,
@@ -130,7 +130,7 @@ def FGPTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
           
     # Running CUDA code here    
     TV_FGP_GPU_main(&inputData[0,0], &outputData[0,0],                        
-                       regularization_parameter, 
+                       regularisation_parameter, 
                        iterations, 
                        tolerance_param,
                        methodTV,
@@ -141,7 +141,7 @@ def FGPTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     return outputData
     
 def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
-                     float regularization_parameter,
+                     float regularisation_parameter,
                      int iterations, 
                      float tolerance_param,
                      int methodTV,
@@ -159,7 +159,7 @@ def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     # Running CUDA code here    
     TV_FGP_GPU_main(            
             &inputData[0,0,0], &outputData[0,0,0], 
-                       regularization_parameter , 
+                       regularisation_parameter , 
                        iterations, 
                        tolerance_param,
                        methodTV,
diff --git a/Wrappers/Python/test/test_cpu_regularizers.py b/Wrappers/Python/test/test_cpu_regularisers.py
index 9713baa..9713baa 100644
--- a/Wrappers/Python/test/test_cpu_regularizers.py
+++ b/Wrappers/Python/test/test_cpu_regularisers.py
diff --git a/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py b/Wrappers/Python/test/test_cpu_vs_gpu_regularisers.py
index 63be1a0..15e9042 100644
--- a/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py
+++ b/Wrappers/Python/test/test_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.regularizers import ROF_TV, FGP_TV
+from ccpi.filters.regularisers import ROF_TV, FGP_TV
 
 ###############################################################################
 def printParametersToString(pars):
@@ -54,7 +54,7 @@ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
 fig = plt.figure(1)
-plt.suptitle('Comparison of ROF-TV regularizer using CPU and GPU implementations')
+plt.suptitle('Comparison of ROF-TV regulariser using CPU and GPU implementations')
 a=fig.add_subplot(1,4,1)
 a.set_title('Noisy Image')
 imgplot = plt.imshow(u0,cmap="gray")
@@ -62,14 +62,14 @@ imgplot = plt.imshow(u0,cmap="gray")
 # set parameters
 pars = {'algorithm': ROF_TV, \
         'input' : u0,\
-        'regularization_parameter':0.04,\
+        'regularisation_parameter':0.04,\
         'number_of_iterations': 1200,\
         'time_marching_parameter': 0.0025        
         }
 print ("#############ROF TV CPU####################")
 start_time = timeit.default_timer()
 rof_cpu = ROF_TV(pars['input'],
-             pars['regularization_parameter'],
+             pars['regularisation_parameter'],
              pars['number_of_iterations'],
              pars['time_marching_parameter'],'cpu')
 rms = rmse(Im, rof_cpu)
@@ -92,7 +92,7 @@ plt.title('{}'.format('CPU results'))
 print ("##############ROF TV GPU##################")
 start_time = timeit.default_timer()
 rof_gpu = ROF_TV(pars['input'], 
-                     pars['regularization_parameter'],
+                     pars['regularisation_parameter'],
                      pars['number_of_iterations'], 
                      pars['time_marching_parameter'],'gpu')
                      
@@ -132,7 +132,7 @@ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
 fig = plt.figure(2)
-plt.suptitle('Comparison of FGP-TV regularizer using CPU and GPU implementations')
+plt.suptitle('Comparison of FGP-TV regulariser using CPU and GPU implementations')
 a=fig.add_subplot(1,4,1)
 a.set_title('Noisy Image')
 imgplot = plt.imshow(u0,cmap="gray")
@@ -140,7 +140,7 @@ imgplot = plt.imshow(u0,cmap="gray")
 # set parameters
 pars = {'algorithm' : FGP_TV, \
         'input' : u0,\
-        'regularization_parameter':0.04, \
+        'regularisation_parameter':0.04, \
         'number_of_iterations' :1200 ,\
         'tolerance_constant':0.00001,\
         'methodTV': 0 ,\
@@ -151,7 +151,7 @@ pars = {'algorithm' : FGP_TV, \
 print ("#############FGP TV CPU####################")
 start_time = timeit.default_timer()
 fgp_cpu = FGP_TV(pars['input'], 
-              pars['regularization_parameter'],
+              pars['regularisation_parameter'],
               pars['number_of_iterations'],
               pars['tolerance_constant'], 
               pars['methodTV'],
@@ -179,7 +179,7 @@ plt.title('{}'.format('CPU results'))
 print ("##############FGP TV GPU##################")
 start_time = timeit.default_timer()
 fgp_gpu = FGP_TV(pars['input'], 
-              pars['regularization_parameter'],
+              pars['regularisation_parameter'],
               pars['number_of_iterations'],
               pars['tolerance_constant'], 
               pars['methodTV'],
diff --git a/Wrappers/Python/test/test_gpu_regularizers.py b/Wrappers/Python/test/test_gpu_regularisers.py
index 640b3f9..2103c0e 100644
--- a/Wrappers/Python/test/test_gpu_regularizers.py
+++ b/Wrappers/Python/test/test_gpu_regularisers.py
@@ -11,8 +11,7 @@ import numpy as np
 import os    
 from enum import Enum
 import timeit
-from ccpi.filters.gpu_regularizers import Diff4thHajiaboli, NML
-from ccpi.filters.regularizers import ROF_TV, FGP_TV
+from ccpi.filters.regularisers import ROF_TV, FGP_TV
 ###############################################################################
 def printParametersToString(pars):
         txt = r''
@@ -32,9 +31,6 @@ def rmse(im1, im2):
     return rmse
         
 filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
-#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
-#filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
-#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
 
 Im = plt.imread(filename)                     
 Im = np.asarray(Im, dtype='float32')
@@ -56,112 +52,20 @@ a.set_title('noise')
 imgplot = plt.imshow(u0,cmap="gray")
 
 
-## Diff4thHajiaboli
-start_time = timeit.default_timer()
-pars = {
-'algorithm' : Diff4thHajiaboli , \
-        'input' : u0,
-        'edge_preserv_parameter':0.1 , \
-'number_of_iterations' :250 ,\
-'time_marching_parameter':0.03 ,\
-'regularization_parameter':0.7
-}
-
-
-d4h = Diff4thHajiaboli(pars['input'], 
-                     pars['edge_preserv_parameter'], 
-                     pars['number_of_iterations'], 
-                     pars['time_marching_parameter'],
-                     pars['regularization_parameter'])
-rms = rmse(Im, d4h)
-pars['rmse'] = rms
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(2,4,2)
-
-# 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=12,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(d4h, cmap="gray")
-
-a=fig.add_subplot(2,4,6)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, 'd4h - u0', transform=a.transAxes, fontsize=12,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow((d4h - u0)**2,  vmin=0, vmax=0.03, cmap="gray")
-plt.colorbar(ticks=[0, 0.03], orientation='vertical')
-
-
-## Patch Based Regul NML
-start_time = timeit.default_timer()
-"""
-pars = {'algorithm' : NML , \
-        'input' : u0,
-        'SearchW_real':3 , \
-'SimilW' :1,\
-'h':0.05 ,#
-'lambda' : 0.08
-}
-"""
-pars = {
-'algorithm' : NML , \
-        'input' : u0,
-        'regularization_parameter': 0.01,\
-        'searching_window_ratio':3, \
-        'similarity_window_ratio':1,\
-        'PB_filtering_parameter': 0.2
-}
-
-nml = NML(pars['input'], 
-                     pars['searching_window_ratio'], 
-                     pars['similarity_window_ratio'], 
-                     pars['PB_filtering_parameter'],
-                     pars['regularization_parameter'])
-rms = rmse(Im, nml)
-pars['rmse'] = rms
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(2,4,3)
-
-# 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=12,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(nml, cmap="gray")
-
-a=fig.add_subplot(2,4,7)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, 'nml - u0', transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow((nml - u0)**2,  vmin=0, vmax=0.03, cmap="gray")
-plt.colorbar(ticks=[0, 0.03], orientation='vertical')
-
-        
         
-## Rudin-Osher-Fatemi (ROF) TV regularization
+## Rudin-Osher-Fatemi (ROF) TV regularisation
 start_time = timeit.default_timer()
 pars = {
 'algorithm' : ROF_TV , \
         'input' : u0,
-        'regularization_parameter': 0.04,\
+        'regularisation_parameter': 0.04,\
         'number_of_iterations':300,\
         'time_marching_parameter': 0.0025
         
 	}
 	
 rof_tv = TV_ROF_GPU(pars['input'], 
-                     pars['regularization_parameter'],
+                     pars['regularisation_parameter'],
                      pars['number_of_iterations'], 
                      pars['time_marching_parameter'],'gpu')
                      
@@ -190,13 +94,13 @@ imgplot = plt.imshow((rof_tv - u0)**2, vmin=0, vmax=0.03, cmap="gray")
 plt.colorbar(ticks=[0, 0.03], orientation='vertical')
 plt.show()
 
-## Fast-Gradient Projection TV regularization
+## Fast-Gradient Projection TV regularisation
 """
 start_time = timeit.default_timer()
 
 pars = {'algorithm' : FGP_TV, \
         'input' : u0,\
-        'regularization_parameter':0.04, \
+        'regularisation_parameter':0.04, \
         'number_of_iterations' :1200 ,\
         'tolerance_constant':0.00001,\
         'methodTV': 0 ,\
@@ -205,7 +109,7 @@ pars = {'algorithm' : FGP_TV, \
         }
 
 fgp_gpu = FGP_TV(pars['input'], 
-              pars['regularization_parameter'],
+              pars['regularisation_parameter'],
               pars['number_of_iterations'],
               pars['tolerance_constant'], 
               pars['methodTV'],
diff --git a/Wrappers/Python/test/test_regularizers_3d.py b/Wrappers/Python/test/test_regularisers_3d.py
index 2d11a7e..2d11a7e 100644
--- a/Wrappers/Python/test/test_regularizers_3d.py
+++ b/Wrappers/Python/test/test_regularisers_3d.py
diff --git a/Wrappers/Python/test/test_regularizers.py b/Wrappers/Python/test/test_regularizers.py
deleted file mode 100644
index cf5da2b..0000000
--- a/Wrappers/Python/test/test_regularizers.py
+++ /dev/null
@@ -1,395 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Aug  4 11:10:05 2017
-
-@author: ofn77899
-"""
-
-#from ccpi.viewer.CILViewer2D import Converter
-#import vtk
-
-import matplotlib.pyplot as plt
-import numpy as np
-import os    
-from enum import Enum
-import timeit
-#from PIL import Image
-#from Regularizer import Regularizer
-from ccpi.filters.Regularizer import Regularizer
-
-###############################################################################
-#https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
-#NRMSE a normalization of the root of the mean squared error
-#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum
-# intensity from the two images being compared, and respectively the same for
-# minval. RMSE is given by the square root of MSE: 
-# sqrt[(sum(A - B) ** 2) / |A|],
-# where |A| means the number of elements in A. By doing this, the maximum value
-# given by RMSE is maxval.
-
-def nrmse(im1, im2):
-    a, b = im1.shape
-    rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b))
-    max_val = max(np.max(im1), np.max(im2))
-    min_val = min(np.min(im1), np.min(im2))
-    return 1 - (rmse / (max_val - min_val))
-###############################################################################
-
-###############################################################################
-#
-#  2D Regularizers
-#
-###############################################################################
-#Example:
-# figure;
-# Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
-
-filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
-#filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
-#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
-
-#reader = vtk.vtkTIFFReader()
-#reader.SetFileName(os.path.normpath(filename))
-#reader.Update()
-Im = plt.imread(filename)                     
-#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255
-#img.show()
-Im = np.asarray(Im, dtype='float32')
-
-
-
-
-#imgplot = plt.imshow(Im)
-perc = 0.05
-u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
-# map the u0 u0->u0>0
-f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-u0 = f(u0).astype('float32')
-
-## plot 
-fig = plt.figure()
-#a=fig.add_subplot(3,3,1)
-#a.set_title('Original')
-#imgplot = plt.imshow(Im)
-
-a=fig.add_subplot(2,3,1)
-a.set_title('noise')
-imgplot = plt.imshow(u0,cmap="gray")
-
-reg_output = []
-##############################################################################
-# Call regularizer
-
-####################### SplitBregman_TV #####################################
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
-start_time = timeit.default_timer()
-reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-print (reg.pars)
-reg.setParameter(input=u0)    
-reg.setParameter(regularization_parameter=10.)
-# or 
-# reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30,
-          #tolerance_constant=1e-4, 
-          #TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-plotme = reg(output_all=True) [0]
-pars = reg.pars
-txtstr = reg.printParametersToString() 
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-    
-
-a=fig.add_subplot(2,3,2)
-
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
-        verticalalignment='top', bbox=props)
-imgplot = plt.imshow(plotme,cmap="gray")
-
-###################### FGP_TV #########################################
-# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
-start_time = timeit.default_timer()
-reg = Regularizer(Regularizer.Algorithm.FGP_TV)
-out2 = reg(input=u0, regularization_parameter=5e-4,
-                          number_of_iterations=10)
-txtstr = reg.printParametersToString()
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)       
-
-
-a=fig.add_subplot(2,3,3)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-imgplot = plt.imshow(out2,cmap="gray")
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
-        verticalalignment='top', bbox=props)
-
-###################### LLT_model #########################################
-# * u0 = Im + .03*randn(size(Im)); % adding noise
-# [Den] = LLT_model(single(u0), 10, 0.1, 1);
-#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
-#input, regularization_parameter , time_step, number_of_iterations,
-#                  tolerance_constant, restrictive_Z_smoothing=0
-
-del out2
-start_time = timeit.default_timer()
-reg = Regularizer(Regularizer.Algorithm.LLT_model)
-out2 = reg(input=u0, regularization_parameter=25,
-                          time_step=0.0003,
-                          tolerance_constant=0.001,
-                          number_of_iterations=300)
-txtstr = reg.printParametersToString()
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(2,3,4)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(out2,cmap="gray")
-
-
-# ###################### PatchBased_Regul #########################################
-# # Quick 2D denoising example in Matlab:   
-# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# #   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
-start_time = timeit.default_timer()
-reg = Regularizer(Regularizer.Algorithm.PatchBased_Regul)
-out2 = reg(input=u0, regularization_parameter=0.05,
-                       searching_window_ratio=3,
-                       similarity_window_ratio=1,
-                       PB_filtering_parameter=0.08)
-txtstr = reg.printParametersToString()
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-
-a=fig.add_subplot(2,3,5)
-
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
-        verticalalignment='top', bbox=props)
-imgplot = plt.imshow(out2,cmap="gray")
-
-
-# ###################### TGV_PD #########################################
-# # Quick 2D denoising example in Matlab:   
-# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# #   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
-
-start_time = timeit.default_timer()
-reg = Regularizer(Regularizer.Algorithm.TGV_PD)
-out2 = reg(input=u0, regularization_parameter=0.05,
-                           first_order_term=1.3,
-                           second_order_term=1,
-                           number_of_iterations=550)
-txtstr = reg.printParametersToString()
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(2,3,6)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(out2,cmap="gray")
-
-
-plt.show()
-
-################################################################################
-##
-##  3D Regularizers
-##
-################################################################################
-##Example:
-## figure;
-## Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
-## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-#
-##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha"
-#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha"
-#
-#reader = vtk.vtkMetaImageReader()
-#reader.SetFileName(os.path.normpath(filename))
-#reader.Update()
-##vtk returns 3D images, let's take just the one slice there is as 2D
-#Im = Converter.vtk2numpy(reader.GetOutput())
-#Im = Im.astype('float32')
-##imgplot = plt.imshow(Im)
-#perc = 0.05
-#u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
-## map the u0 u0->u0>0
-#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-#u0 = f(u0).astype('float32')
-#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(),
-#                                        reader.GetOutput().GetOrigin())
-#converter.Update()
-#writer = vtk.vtkMetaImageWriter()
-#writer.SetInputData(converter.GetOutput())
-#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha")
-##writer.Write()
-#
-#
-### plot 
-#fig3D = plt.figure()
-##a=fig.add_subplot(3,3,1)
-##a.set_title('Original')
-##imgplot = plt.imshow(Im)
-#sliceNo = 32
-#
-#a=fig3D.add_subplot(2,3,1)
-#a.set_title('noise')
-#imgplot = plt.imshow(u0.T[sliceNo])
-#
-#reg_output3d = []
-#
-###############################################################################
-## Call regularizer
-#
-######################## SplitBregman_TV #####################################
-## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-#
-##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-#
-##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
-##          #tolerance_constant=1e-4, 
-##          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-#
-#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
-#          tolerance_constant=1e-4, 
-#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-#
-#
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-#        verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### FGP_TV #########################################
-## u = FGP_TV(single(u0), 0.05, 100, 1e-04);
-#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
-#                          number_of_iterations=200)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-#        verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### LLT_model #########################################
-## * u0 = Im + .03*randn(size(Im)); % adding noise
-## [Den] = LLT_model(single(u0), 10, 0.1, 1);
-##Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
-##input, regularization_parameter , time_step, number_of_iterations,
-##                  tolerance_constant, restrictive_Z_smoothing=0
-#out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
-#                          time_step=0.0003,
-#                          tolerance_constant=0.0001,
-#                          number_of_iterations=300)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-#        verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### PatchBased_Regul #########################################
-## Quick 2D denoising example in Matlab:   
-##   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-##   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-##   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
-#
-#out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
-#                          searching_window_ratio=3,
-#                          similarity_window_ratio=1,
-#                          PB_filtering_parameter=0.08)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-#        verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-
-###################### TGV_PD #########################################
-# Quick 2D denoising example in Matlab:   
-#   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-#   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-#   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
-
-
-#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
-#                          first_order_term=1.3,
-#                          second_order_term=1,
-#                          number_of_iterations=550)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-#        verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
diff --git a/recipes/regularizers/bld.bat b/recipes/regularisers/bld.bat
index 6f2f7e7..6f2f7e7 100644
--- a/recipes/regularizers/bld.bat
+++ b/recipes/regularisers/bld.bat
diff --git a/recipes/regularizers/build.sh b/recipes/regularisers/build.sh
index 0b8bce2..0b8bce2 100644
--- a/recipes/regularizers/build.sh
+++ b/recipes/regularisers/build.sh
diff --git a/recipes/regularizers/meta.yaml b/recipes/regularisers/meta.yaml
index 37e447e..f204a6b 100644
--- a/recipes/regularizers/meta.yaml
+++ b/recipes/regularisers/meta.yaml
@@ -1,5 +1,5 @@
 package:
-  name: cil_regularizer
+  name: cil_regulariser
   version: {{ environ['CIL_VERSION'] }}
 
 #source:
@@ -37,4 +37,4 @@ requirements:
 about:
   home: http://www.ccpi.ac.uk
   license: Apache v2.0
-  summary: Regularizer package from CCPi
+  summary: Regulariser package from CCPi