Merge pull request #99 from vais-ral/TGV_bugfix

TGV bugfix

3 files changed, 38 insertions, 26 deletions

diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
index 0c331a4..ac8e1ba 100644
--- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -8,7 +8,7 @@ addpath(Path2);
 addpath(Path3);
 
 N = 512; 
-slices = 7;
+slices = 15;
 vol3D = zeros(N,N,slices, 'single');
 Ideal3D = zeros(N,N,slices, 'single');
 Im = double(imread('lena_gray_512.tif'))/255;  % loading image
@@ -17,9 +17,7 @@ vol3D(:,:,i) = Im + .05*randn(size(Im));
 Ideal3D(:,:,i) = Im;
 end
 vol3D(vol3D < 0) = 0;
-figure; imshow(vol3D(:,:,15), [0 1]); title('Noisy image');
-
-
+figure; imshow(vol3D(:,:,7), [0 1]); title('Noisy image');
 lambda_reg = 0.03; % regularsation parameter for all methods
 %%
 fprintf('Denoise a volume using the ROF-TV model (CPU) \n');
@@ -143,6 +141,16 @@ 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)');
 %%
+% fprintf('Denoise using the TGV model (GPU) \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
+% iter_TGV = 500; % number of Primal-Dual iterations for TGV
+% tic; u_tgv_gpu = TGV_GPU(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV); 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)');
+%%
 %>>>>>>>>>>>>>> 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 14d3096..62e5834 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -5,7 +5,9 @@ fsep = '/';
 Path1 = sprintf(['..' fsep 'mex_compile' fsep 'installed'], 1i);
 Path2 = sprintf(['..' fsep '..' fsep '..' fsep 'data' fsep], 1i);
 Path3 = sprintf(['..' fsep 'supp'], 1i);
-addpath(Path1); addpath(Path2); addpath(Path3);
+addpath(Path1); 
+addpath(Path2); 
+addpath(Path3);
 
 Im = double(imread('lena_gray_512.tif'))/255;  % loading image
 u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
@@ -29,7 +31,7 @@ figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)');
 % figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)');
 %%
 fprintf('Denoise using the FGP-TV model (CPU) \n');
-iter_fgp = 1000; % number of FGP iterations
+iter_fgp = 1300; % number of FGP iterations
 epsil_tol =  1.0e-06; % tolerance
 tic; u_fgp = FGP_TV(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; 
 energyfunc_val_fgp = TV_energy(single(u_fgp),single(u0),lambda_reg, 1); % get energy function value
@@ -39,8 +41,8 @@ figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)');
 
 %%
 % fprintf('Denoise using the FGP-TV model (GPU) \n');
-% iter_fgp = 1000; % number of FGP iterations
-% epsil_tol =  1.0e-05; % tolerance
+% iter_fgp = 1300; % number of FGP iterations
+% epsil_tol =  1.0e-06; % tolerance
 % tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; 
 % figure; imshow(u_fgpG, [0 1]); title('FGP-TV denoised image (GPU)');
 %%
@@ -63,17 +65,17 @@ fprintf('Denoise using the TGV model (CPU) \n');
 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
+iter_TGV = 1500; % 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 = 0.045; % 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
+% iter_TGV = 1500; % 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_GPU/TGV_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
index edb551d..1173282 100644
--- a/Wrappers/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
+++ b/Wrappers/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
@@ -21,18 +21,18 @@ limitations under the License.
 #include "TGV_GPU_core.h"
 
 /* CUDA 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:
- * Filtered/regulariaed image 
+ * Filtered/regularised image 
  *
  * References:
  * [1] K. Bredies "Total Generalized Variation"
@@ -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;
     
@@ -57,8 +57,8 @@ void mexFunction(
     Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D) */
     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 */
-    iter =  300; /* Iterations number */      
+    alpha0 =  2.0f; /* parameter to control the second-order term */
+    iter =  500; /* Iterations number */      
     L2 =  12.0f; /* Lipshitz constant */
     
     if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }   
@@ -68,12 +68,14 @@ 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_GPU_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_GPU_main(Input, Output, lambda, alpha1, alpha0, iter, L2, dimX, dimY, dimZ);        
 }