matlab demos updated

3 files changed, 30 insertions, 28 deletions

diff --git a/demos/demoMatlab_3Ddenoise.m b/demos/demoMatlab_3Ddenoise.m
index 6b21e86..3942eea 100644
--- a/demos/demoMatlab_3Ddenoise.m
+++ b/demos/demoMatlab_3Ddenoise.m
@@ -145,9 +145,10 @@ fprintf('Denoise using the TGV model (CPU) \n');
 lambda_TGV = 0.03; % regularisation parameter
 alpha1 = 1.0; % parameter to control the first-order term
 alpha0 = 2.0; % parameter to control the second-order term
+L2 =  12.0; % convergence parameter
 iter_TGV = 500; % number of Primal-Dual iterations for TGV
 epsil_tol =  0.0; % tolerance
-tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, epsil_tol); toc; 
+tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, L2, epsil_tol); toc; 
 rmseTGV = RMSE(Ideal3D(:),u_tgv(:));
 fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
 figure; imshow(u_tgv(:,:,3), [0 1]); title('TGV denoised volume (CPU)');
@@ -157,7 +158,7 @@ figure; imshow(u_tgv(:,:,3), [0 1]); title('TGV denoised volume (CPU)');
 % alpha1 = 1.0; % parameter to control the first-order term
 % alpha0 = 2.0; % parameter to control the second-order term
 % iter_TGV = 500; % number of Primal-Dual iterations for TGV
-% tic; u_tgv_gpu = TGV_GPU(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, epsil_tol); toc; 
+% tic; u_tgv_gpu = TGV_GPU(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, L2, epsil_tol); toc; 
 % rmseTGV = RMSE(Ideal3D(:),u_tgv_gpu(:));
 % fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
 % figure; imshow(u_tgv_gpu(:,:,3), [0 1]); title('TGV denoised volume (GPU)');
diff --git a/demos/demoMatlab_denoise.m b/demos/demoMatlab_denoise.m
index 377a447..9d89138 100644
--- a/demos/demoMatlab_denoise.m
+++ b/demos/demoMatlab_denoise.m
@@ -83,17 +83,18 @@ fprintf('Denoise using the TGV model (CPU) \n');
 lambda_TGV = 0.035; % regularisation parameter
 alpha1 = 1.0; % parameter to control the first-order term
 alpha0 = 2.0; % parameter to control the second-order term
-iter_TGV = 20; % number of Primal-Dual iterations for TGV
+L2 =  12.0; % convergence parameter
+iter_TGV = 1200; % number of Primal-Dual iterations for TGV
 epsil_tol =  0.0; % tolerance
-tic; [u_tgv,infovec] = TGV(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV, epsil_tol); toc; 
+tic; [u_tgv,infovec] = TGV(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV, L2, epsil_tol); toc; 
+figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)');
 rmseTGV = (RMSE(u_tgv(:),Im(:)));
 fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
 [ssimval] = ssim(u_tgv*255,single(Im)*255);
 fprintf('%s %f \n', 'MSSIM error for TGV is:', ssimval);
-figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)');
 %%
 % fprintf('Denoise using the TGV model (GPU) \n');
-% tic; u_tgv_gpu = TGV_GPU(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV, epsil_tol); toc; 
+% tic; u_tgv_gpu = TGV_GPU(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV, L2, epsil_tol); toc; 
 % figure; imshow(u_tgv_gpu, [0 1]); title('TGV denoised image (GPU)');
 %%
 fprintf('Denoise using the ROF-LLT model (CPU) \n');
diff --git a/src/Matlab/mex_compile/regularisers_CPU/TGV.c b/src/Matlab/mex_compile/regularisers_CPU/TGV.c
index 2c0fcbd..9e32ae4 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/TGV.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/TGV.c
@@ -20,7 +20,7 @@ limitations under the License.
 #include "mex.h"
 #include "TGV_core.h"
 
-/* C-OMP implementation of Primal-Dual denoising method for 
+/* C-OMP implementation of Primal-Dual denoising method for
  * Total Generilized Variation (TGV)-L2 model [1] (2D/3D)
  *
  * Input Parameters:
@@ -33,7 +33,7 @@ limitations under the License.
  * 7. eplsilon - tolerance constant [OPTIONAL parameter]
  *
  * Output:
- * [1] Regularized image/volume 
+ * [1] Regularized image/volume
  * [2] Information vector which contains [iteration no., reached tolerance]
  *
  *
@@ -44,51 +44,51 @@ limitations under the License.
 void mexFunction(
         int nlhs, mxArray *plhs[],
         int nrhs, const mxArray *prhs[])
-        
+
 {
     int number_of_dims, iter;
     mwSize dimX, dimY, dimZ;
     const mwSize *dim_array;
-    
+
     float *Input, *Output=NULL, lambda, alpha0, alpha1, L2, epsil;
     float *infovec=NULL;
-    
+
     number_of_dims = mxGetNumberOfDimensions(prhs[0]);
     dim_array = mxGetDimensions(prhs[0]);
-    
+
     /*Handling Matlab input data*/
-    if ((nrhs < 2) || (nrhs > 7)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D), Regularisation parameter, alpha0, alpha1, iterations number, Lipshitz Constant");
-    
+    if ((nrhs < 2) || (nrhs > 7)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D), Regularisation parameter, alpha0, alpha1, iterations number, Lipshitz Constant, tolerance");
+
     Input  = (float *) mxGetData(prhs[0]); /*noisy image/volume */
     lambda =  (float) mxGetScalar(prhs[1]); /* regularisation parameter */
-    alpha1 =  1.0f; /* parameter to control the first-order term */ 
+    alpha1 =  1.0f; /* parameter to control the first-order term */
     alpha0 =  2.0f; /* parameter to control the second-order term */
-    iter =  500; /* Iterations number */      
+    iter =  500; /* Iterations number */
     L2 =  12.0f; /* Lipshitz constant */
     epsil = 1.0e-06; /*tolerance parameter*/
-    
-    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) || (nrhs == 7))  alpha1 =  (float) mxGetScalar(prhs[2]); /* parameter to control the first-order term */ 
+
+    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) || (nrhs == 7))  alpha1 =  (float) mxGetScalar(prhs[2]); /* parameter to control the first-order term */
     if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7))  alpha0 =  (float) mxGetScalar(prhs[3]);  /* parameter to control the second-order term */
-    if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7))  iter =  (int) mxGetScalar(prhs[4]); /* Iterations number */      
+    if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7))  iter =  (int) mxGetScalar(prhs[4]); /* Iterations number */
     if ((nrhs == 6) || (nrhs == 7))  L2 =  (float) mxGetScalar(prhs[5]); /* Lipshitz constant */
     if (nrhs == 7) epsil =  (float) mxGetScalar(prhs[6]); /* epsilon */
-    
+
     /*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));        
+        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));
-    }    
- 
+    }
+
     mwSize vecdim[1];
     vecdim[0] = 2;
-    infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));    
-    
+    infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
+
     /* running the function */
-    TGV_main(Input, Output, infovec, lambda, alpha1, alpha0, iter, L2, epsil, dimX, dimY, dimZ);        
+    TGV_main(Input, Output, infovec, lambda, alpha1, alpha0, iter, L2, epsil, dimX, dimY, dimZ);
 }