first TGVD version

3 files changed, 37 insertions, 23 deletions

diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
index 5cc47b3..23cda32 100644
--- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -2,11 +2,13 @@
 clear; close all
 Path1 = sprintf(['..' filesep 'mex_compile' filesep 'installed'], 1i);
 Path2 = sprintf(['..' filesep '..' filesep '..' filesep 'data' filesep], 1i);
+Path3 = sprintf(['..' filesep 'supp'], 1i);
 addpath(Path1);
 addpath(Path2);
+addpath(Path3);
 
 N = 512; 
-slices = 15;
+slices = 7;
 vol3D = zeros(N,N,slices, 'single');
 Ideal3D = zeros(N,N,slices, 'single');
 Im = double(imread('lena_gray_512.tif'))/255;  % loading image
@@ -131,7 +133,16 @@ figure; imshow(u_diff4(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (CP
 % 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)');
 %%
-
+fprintf('Denoise using the TGV model (CPU) \n');
+lambda_TGV = 0.05; % regularisation parameter
+alpha1 = 1.0; % parameter to control the first-order term
+alpha0 = 2.0; % parameter to control the second-order term
+iter_TGV = 40; % number of Primal-Dual iterations for TGV
+tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, 128); 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)');
+%%
 %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
 fprintf('Denoise a volume using the FGP-dTV model (CPU) \n');
 
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index 3506cca..14d3096 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -60,20 +60,20 @@ figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)');
 % figure; imshow(u_sbG, [0 1]); title('SB-TV denoised image (GPU)');
 %%
 fprintf('Denoise using the TGV model (CPU) \n');
-lambda_TGV = 0.04; % regularisation parameter
-alpha1 = 1; % parameter to control the first-order term
-alpha0 = 0.7; % parameter to control the second-order term
-iter_TGV = 500; % number of Primal-Dual iterations for TGV
+lambda_TGV = 0.045; % regularisation parameter
+alpha1 = 1.0; % parameter to control the first-order term
+alpha0 = 2.0; % parameter to control the second-order term
+iter_TGV = 2000; % number of Primal-Dual iterations for TGV
 tic; u_tgv = TGV(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV); toc; 
 rmseTGV = (RMSE(u_tgv(:),Im(:)));
 fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
 figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)');
 %%
 % fprintf('Denoise using the TGV model (GPU) \n');
-% lambda_TGV = 0.04; % regularisation parameter
-% alpha1 = 1; % parameter to control the first-order term
-% alpha0 = 0.7; % parameter to control the second-order term
-% iter_TGV = 500; % number of Primal-Dual iterations for TGV
+% lambda_TGV = 0.045; % regularisation parameter
+% alpha1 = 1.0; % parameter to control the first-order term
+% alpha0 = 2.0; % parameter to control the second-order term
+% iter_TGV = 2000; % number of Primal-Dual iterations for TGV
 % tic; u_tgv_gpu = TGV_GPU(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV); toc; 
 % rmseTGV_gpu = (RMSE(u_tgv_gpu(:),Im(:)));
 % fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV_gpu);
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/TGV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/TGV.c
index 5459bf5..aa4eed4 100644
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/TGV.c
+++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/TGV.c
@@ -21,14 +21,14 @@ limitations under the License.
 #include "TGV_core.h"
 
 /* C-OMP implementation of Primal-Dual denoising method for 
- * Total Generilized Variation (TGV)-L2 model [1] (2D case only)
+ * Total Generilized Variation (TGV)-L2 model [1] (2D/3D)
  *
  * Input Parameters:
- * 1. Noisy image (2D) (required)
- * 2. lambda - regularisation parameter (required)
- * 3. parameter to control the first-order term (alpha1) (default - 1)
- * 4. parameter to control the second-order term (alpha0) (default - 0.5)
- * 5. Number of Chambolle-Pock (Primal-Dual) iterations (default is 300)
+ * 1. Noisy image/volume (2D/3D)
+ * 2. lambda - regularisation parameter
+ * 3. parameter to control the first-order term (alpha1)
+ * 4. parameter to control the second-order term (alpha0)
+ * 5. Number of Chambolle-Pock (Primal-Dual) iterations
  * 6. Lipshitz constant (default is 12)
  *
  * Output:
@@ -44,7 +44,7 @@ void mexFunction(
         
 {
     int number_of_dims, iter;
-    mwSize dimX, dimY;
+    mwSize dimX, dimY, dimZ;
     const mwSize *dim_array;
     
     float *Input, *Output=NULL, lambda, alpha0, alpha1, L2;
@@ -55,7 +55,7 @@ void mexFunction(
     /*Handling Matlab input data*/
     if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D), Regularisation parameter, alpha0, alpha1, iterations number, Lipshitz Constant");
     
-    Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D) */
+    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 */ 
     alpha0 =  0.5f; /* parameter to control the second-order term */
@@ -69,12 +69,15 @@ void mexFunction(
     if (nrhs == 6)  L2 =  (float) mxGetScalar(prhs[5]); /* Lipshitz constant */
     
     /*Handling Matlab output data*/
-    dimX = dim_array[0]; dimY = dim_array[1];
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
     
     if (number_of_dims == 2) {
-        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        /* running the function */
-        TGV_main(Input, Output, lambda, alpha1, alpha0, iter, L2, dimX, dimY);        
+        dimZ = 1; /*2D case*/
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));        
     }
-    if (number_of_dims == 3) {mexErrMsgTxt("Only 2D images accepted");}       
+    if (number_of_dims == 3) {
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    }       
+    /* running the function */
+    TGV_main(Input, Output, lambda, alpha1, alpha0, iter, L2, dimX, dimY, dimZ);        
 }