some corrections to energy estimation

2 files changed, 6 insertions, 6 deletions

diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
index 84889d7..5a54d18 100644
--- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -21,7 +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
+energyfunc_val_rof = TV_energy(single(u_rof),single(vol3D),lambda_reg, 1);  % 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');
@@ -34,7 +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
+energyfunc_val_fgp = TV_energy(single(u_fgp),single(vol3D),lambda_reg, 1); % 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');
@@ -47,7 +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
+energyfunc_val_sb = TV_energy(single(u_sb),single(vol3D),lambda_reg, 1);  % 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 526d21c..151a604 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -14,7 +14,7 @@ fprintf('Denoise using the ROF-TV model (CPU) \n');
 tau_rof = 0.0025; % time-marching constant 
 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
+energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg, 1);  % 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');
@@ -27,7 +27,7 @@ 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
+energyfunc_val_fgp = TV_energy(single(u_fgp),single(u0),lambda_reg, 1); % get energy function value
 figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)');
 
 %%
@@ -41,7 +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
+energyfunc_val_sb = TV_energy(single(u_sb),single(u0),lambda_reg, 1);  % 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');