bug fixes, demos updates

2 files changed, 21 insertions, 11 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);