Merge pull request #50 from vais-ral/SB_TV

Split Bregman method

2 files changed, 43 insertions, 24 deletions

diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
index dc49d9c..fb55097 100644
--- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -14,35 +14,47 @@ end
 vol3D(vol3D < 0) = 0;
 figure; imshow(vol3D(:,:,15), [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');
-lambda_rof = 0.03; % regularisation parameter
 tau_rof = 0.0025; % time-marching constant 
 iter_rof = 300; % number of ROF iterations
-tic; u_rof = ROF_TV(single(vol3D), lambda_rof, iter_rof, tau_rof); toc; 
+tic; u_rof = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof); toc; 
 figure; imshow(u_rof(:,:,15), [0 1]); title('ROF-TV denoised volume (CPU)');
 %%
 % fprintf('Denoise a volume using the ROF-TV model (GPU) \n');
-% lambda_rof = 0.03; % regularisation parameter
 % tau_rof = 0.0025; % time-marching constant 
 % iter_rof = 300; % number of ROF iterations
-% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_rof, iter_rof, tau_rof); toc;
+% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_reg, iter_rof, tau_rof); toc;
 % figure; imshow(u_rofG(:,:,15), [0 1]); title('ROF-TV denoised volume (GPU)');
 %%
 fprintf('Denoise a volume using the FGP-TV model (CPU) \n');
-lambda_fgp = 0.03; % regularisation parameter
 iter_fgp = 300; % number of FGP iterations
 epsil_tol =  1.0e-05; % tolerance
-tic; u_fgp = FGP_TV(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; 
+tic; u_fgp = FGP_TV(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; 
 figure; imshow(u_fgp(:,:,15), [0 1]); title('FGP-TV denoised volume (CPU)');
 %%
 % fprintf('Denoise a volume using the FGP-TV model (GPU) \n');
-% lambda_fgp = 0.03; % regularisation parameter
 % iter_fgp = 300; % number of FGP iterations
 % epsil_tol =  1.0e-05; % tolerance
-% tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; 
+% tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; 
 % figure; imshow(u_fgpG(:,:,15), [0 1]); title('FGP-TV denoised volume (GPU)');
 %%
+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; 
+figure; imshow(u_sb(:,:,15), [0 1]); title('SB-TV denoised volume (CPU)');
+%%
+% fprintf('Denoise a volume using the SB-TV model (GPU) \n');
+% iter_sb = 150; % number of SB iterations
+% epsil_tol =  1.0e-05; % tolerance
+% tic; u_sbG = SB_TV_GPU(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; 
+% figure; imshow(u_sbG(:,:,15), [0 1]); title('SB-TV denoised volume (GPU)');
+%%
+
+%>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
 fprintf('Denoise a volume using the FGP-dTV model (CPU) \n');
 
 % create another volume (reference) with slightly less amount of noise
@@ -53,11 +65,10 @@ end
 vol3D_ref(vol3D_ref < 0) = 0;
 % vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV)
 
-lambda_fgp = 0.03; % regularisation parameter
 iter_fgp = 300; % number of FGP iterations
 epsil_tol =  1.0e-05; % tolerance
 eta =  0.2; % Reference image gradient smoothing constant
-tic; u_fgp_dtv = FGP_dTV(single(vol3D), single(vol3D_ref), lambda_fgp, iter_fgp, epsil_tol, eta); toc; 
+tic; u_fgp_dtv = FGP_dTV(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
 figure; imshow(u_fgp_dtv(:,:,15), [0 1]); title('FGP-dTV denoised volume (CPU)');
 %%
 fprintf('Denoise a volume using the FGP-dTV model (GPU) \n');
@@ -70,10 +81,9 @@ end
 vol3D_ref(vol3D_ref < 0) = 0;
 % vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV)
 
-lambda_fgp = 0.03; % regularisation parameter
 iter_fgp = 300; % number of FGP iterations
 epsil_tol =  1.0e-05; % tolerance
 eta =  0.2; % Reference image gradient smoothing constant
-tic; u_fgp_dtv_g = FGP_dTV_GPU(single(vol3D), single(vol3D_ref), lambda_fgp, iter_fgp, epsil_tol, eta); toc; 
+tic; u_fgp_dtv_g = FGP_dTV_GPU(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
 figure; imshow(u_fgp_dtv_g(:,:,15), [0 1]); title('FGP-dTV denoised volume (GPU)');
 %%
\ No newline at end of file
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index 145f2ff..129bedc 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -8,45 +8,55 @@ Im = double(imread('lena_gray_512.tif'))/255;  % loading image
 u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
 figure; imshow(u0, [0 1]); title('Noisy image');
 
+lambda_reg = 0.03; % regularsation parameter for all methods
 %%
 fprintf('Denoise using the ROF-TV model (CPU) \n');
-lambda_rof = 0.03; % regularisation parameter
 tau_rof = 0.0025; % time-marching constant 
 iter_rof = 2000; % number of ROF iterations
-tic; u_rof = ROF_TV(single(u0), lambda_rof, iter_rof, tau_rof); toc; 
+tic; u_rof = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof); toc; 
 figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)');
 %%
 % fprintf('Denoise using the ROF-TV model (GPU) \n');
-% lambda_rof = 0.03; % regularisation parameter
 % tau_rof = 0.0025; % time-marching constant 
 % iter_rof = 2000; % number of ROF iterations
-% tic; u_rofG = ROF_TV_GPU(single(u0), lambda_rof, iter_rof, tau_rof); toc;
+% 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)');
 %%
 fprintf('Denoise using the FGP-TV model (CPU) \n');
-lambda_fgp = 0.03; % regularisation parameter
 iter_fgp = 1000; % number of FGP iterations
 epsil_tol =  1.0e-06; % tolerance
-tic; u_fgp = FGP_TV(single(u0), lambda_fgp, iter_fgp, epsil_tol); toc; 
+tic; u_fgp = FGP_TV(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; 
 figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)');
 %%
 % fprintf('Denoise using the FGP-TV model (GPU) \n');
-% lambda_fgp = 0.03; % regularisation parameter
 % iter_fgp = 1000; % number of FGP iterations
 % epsil_tol =  1.0e-05; % tolerance
-% tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_fgp, iter_fgp, epsil_tol); toc; 
+% 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)');
 %%
+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; 
+figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)');
+%%
+% fprintf('Denoise using the SB-TV model (GPU) \n');
+% iter_sb = 150; % number of SB iterations
+% epsil_tol =  1.0e-06; % tolerance
+% tic; u_sbG = SB_TV_GPU(single(u0), lambda_reg, iter_sb, epsil_tol); toc; 
+% figure; imshow(u_sbG, [0 1]); title('SB-TV denoised image (GPU)');
+%%
+%>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
+
 fprintf('Denoise using the FGP-dTV model (CPU) \n');
 % create another image (reference) with slightly less amount of noise
 u_ref = Im + .01*randn(size(Im)); u_ref(u_ref < 0) = 0;
 % u_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV)
 
-lambda_fgp = 0.03; % regularisation parameter
 iter_fgp = 1000; % number of FGP iterations
 epsil_tol =  1.0e-06; % tolerance
 eta =  0.2; % Reference image gradient smoothing constant
-tic; u_fgp_dtv = FGP_dTV(single(u0), single(u_ref), lambda_fgp, iter_fgp, epsil_tol, eta); toc; 
+tic; u_fgp_dtv = FGP_dTV(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
 figure; imshow(u_fgp_dtv, [0 1]); title('FGP-dTV denoised image (CPU)');
 %%
 % fprintf('Denoise using the FGP-dTV model (GPU) \n');
@@ -54,10 +64,9 @@ figure; imshow(u_fgp_dtv, [0 1]); title('FGP-dTV denoised image (CPU)');
 % u_ref = Im + .01*randn(size(Im)); u_ref(u_ref < 0) = 0;
 % % u_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV)
 % 
-% lambda_fgp = 0.03; % regularisation parameter
 % iter_fgp = 1000; % number of FGP iterations
 % epsil_tol =  1.0e-06; % tolerance
 % eta =  0.2; % Reference image gradient smoothing constant
-% tic; u_fgp_dtvG = FGP_dTV_GPU(single(u0), single(u_ref), lambda_fgp, iter_fgp, epsil_tol, eta); toc; 
+% tic; u_fgp_dtvG = FGP_dTV_GPU(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
 % figure; imshow(u_fgp_dtvG, [0 1]); title('FGP-dTV denoised image (GPU)');
 %%