2 files changed, 9 insertions, 2 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
index 973d060..84889d7 100644
--- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -21,6 +21,7 @@ fprintf('Denoise a volume using the ROF-TV model (CPU) \n');
 tau_rof = 0.0025; % time-marching constant 
 iter_rof = 300; % number of ROF iterations
 tic; u_rof = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof); toc; 
+energyfunc_val_rof = TV_energy(single(u_rof),single(vol3D),lambda_reg);  % get energy function value
 figure; imshow(u_rof(:,:,15), [0 1]); title('ROF-TV denoised volume (CPU)');
 %%
 % fprintf('Denoise a volume using the ROF-TV model (GPU) \n');
@@ -33,6 +34,7 @@ fprintf('Denoise a volume using the FGP-TV model (CPU) \n');
 iter_fgp = 300; % number of FGP iterations
 epsil_tol =  1.0e-05; % tolerance
 tic; u_fgp = FGP_TV(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; 
+energyfunc_val_fgp = TV_energy(single(u_fgp),single(vol3D),lambda_reg); % get energy function value
 figure; imshow(u_fgp(:,:,15), [0 1]); title('FGP-TV denoised volume (CPU)');
 %%
 % fprintf('Denoise a volume using the FGP-TV model (GPU) \n');
@@ -45,6 +47,7 @@ fprintf('Denoise a volume using the SB-TV model (CPU) \n');
 iter_sb = 150; % number of SB iterations
 epsil_tol =  1.0e-05; % tolerance
 tic; u_sb = SB_TV(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; 
+energyfunc_val_sb = TV_energy(single(u_sb),single(vol3D),lambda_reg);  % get energy function value
 figure; imshow(u_sb(:,:,15), [0 1]); title('SB-TV denoised volume (CPU)');
 %%
 % fprintf('Denoise a volume using the SB-TV model (GPU) \n');
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index 4a0a19a..526d21c 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -12,13 +12,14 @@ lambda_reg = 0.03; % regularsation parameter for all methods
 %%
 fprintf('Denoise using the ROF-TV model (CPU) \n');
 tau_rof = 0.0025; % time-marching constant 
-iter_rof = 2000; % number of ROF iterations
+iter_rof = 750; % number of ROF iterations
 tic; u_rof = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof); toc; 
+energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg);  % get energy function value
 figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)');
 %%
 % fprintf('Denoise using the ROF-TV model (GPU) \n');
 % tau_rof = 0.0025; % time-marching constant 
-% iter_rof = 2000; % number of ROF iterations
+% iter_rof = 750; % number of ROF iterations
 % tic; u_rofG = ROF_TV_GPU(single(u0), lambda_reg, iter_rof, tau_rof); toc;
 % figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)');
 %%
@@ -26,7 +27,9 @@ fprintf('Denoise using the FGP-TV model (CPU) \n');
 iter_fgp = 1000; % 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); % get energy function value
 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
@@ -38,6 +41,7 @@ fprintf('Denoise using the SB-TV model (CPU) \n');
 iter_sb = 150; % number of SB iterations
 epsil_tol =  1.0e-06; % tolerance
 tic; u_sb = SB_TV(single(u0), lambda_reg, iter_sb, epsil_tol); toc; 
+energyfunc_val_sb = TV_energy(single(u_sb),single(u0),lambda_reg);  % get energy function value
 figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)');
 %%
 % fprintf('Denoise using the SB-TV model (GPU) \n');