diff options
Diffstat (limited to 'demos')
| -rw-r--r-- | demos/demoMatlab_denoise.m | 104 |
1 files changed, 61 insertions, 43 deletions
diff --git a/demos/demoMatlab_denoise.m b/demos/demoMatlab_denoise.m index 5135129..5e92ee1 100644 --- a/demos/demoMatlab_denoise.m +++ b/demos/demoMatlab_denoise.m @@ -13,87 +13,119 @@ 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_reg = 0.017; % regularsation parameter for all methods tau_rof = 0.0025; % time-marching constant -iter_rof = 750; % number of ROF iterations +iter_rof = 1200; % 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, 1); % get energy function value rmseROF = (RMSE(u_rof(:),Im(:))); fprintf('%s %f \n', 'RMSE error for ROF-TV is:', rmseROF); +[ssimval] = ssim(u_rof*255,single(Im)*255); +fprintf('%s %f \n', 'MSSIM error for ROF-TV is:', ssimval); 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 = 750; % number of ROF iterations +% iter_rof = 1200; % 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)'); %% fprintf('Denoise using the FGP-TV model (CPU) \n'); -iter_fgp = 1300; % number of FGP iterations -epsil_tol = 1.0e-06; % tolerance +lambda_reg = 0.033; +iter_fgp = 300; % number of FGP iterations +epsil_tol = 1.0e-09; % 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 rmseFGP = (RMSE(u_fgp(:),Im(:))); fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmseFGP); +[ssimval] = ssim(u_fgp*255,single(Im)*255); +fprintf('%s %f \n', 'MSSIM error for FGP-TV is:', ssimval); figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)'); - %% % fprintf('Denoise using the FGP-TV model (GPU) \n'); -% iter_fgp = 1300; % number of FGP iterations -% epsil_tol = 1.0e-06; % tolerance +% iter_fgp = 300; % number of FGP iterations +% epsil_tol = 1.0e-09; % 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)'); %% fprintf('Denoise using the SB-TV model (CPU) \n'); -iter_sb = 150; % number of SB iterations -epsil_tol = 1.0e-06; % tolerance +iter_sb = 80; % number of SB iterations +epsil_tol = 1.0e-08; % 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, 1); % get energy function value rmseSB = (RMSE(u_sb(:),Im(:))); fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmseSB); +[ssimval] = ssim(u_sb*255,single(Im)*255); +fprintf('%s %f \n', 'MSSIM error for SB-TV is:', ssimval); 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 +% iter_sb = 80; % 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)'); %% +fprintf('Denoise using Nonlinear-Diffusion model (CPU) \n'); +iter_diff = 450; % number of diffusion iterations +lambda_regDiff = 0.025; % regularisation for the diffusivity +sigmaPar = 0.015; % edge-preserving parameter +tau_param = 0.02; % time-marching constant +tic; u_diff = NonlDiff(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; +rmseDiffus = (RMSE(u_diff(:),Im(:))); +fprintf('%s %f \n', 'RMSE error for Nonlinear Diffusion is:', rmseDiffus); +[ssimval] = ssim(u_diff*255,single(Im)*255); +fprintf('%s %f \n', 'MSSIM error for NDF is:', ssimval); +figure; imshow(u_diff, [0 1]); title('Diffusion denoised image (CPU)'); +%% +% fprintf('Denoise using Nonlinear-Diffusion model (GPU) \n'); +% iter_diff = 450; % number of diffusion iterations +% lambda_regDiff = 0.025; % regularisation for the diffusivity +% sigmaPar = 0.015; % edge-preserving parameter +% tau_param = 0.025; % time-marching constant +% tic; u_diff_g = NonlDiff_GPU(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; +% figure; imshow(u_diff_g, [0 1]); title('Diffusion denoised image (GPU)'); +%% fprintf('Denoise using the TGV model (CPU) \n'); -lambda_TGV = 0.045; % regularisation parameter +lambda_TGV = 0.034; % regularisation parameter alpha1 = 1.0; % parameter to control the first-order term -alpha0 = 2.0; % parameter to control the second-order term -iter_TGV = 1500; % number of Primal-Dual iterations for TGV +alpha0 = 1.0; % parameter to control the second-order term +iter_TGV = 500; % 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); +[ssimval] = ssim(u_tgv*255,single(Im)*255); +fprintf('%s %f \n', 'MSSIM error for TGV is:', ssimval); 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 +% lambda_TGV = 0.034; % regularisation parameter % alpha1 = 1.0; % parameter to control the first-order term -% alpha0 = 2.0; % parameter to control the second-order term -% iter_TGV = 1500; % number of Primal-Dual iterations for TGV +% alpha0 = 1.0; % parameter to control the second-order term +% iter_TGV = 500; % number of Primal-Dual iterations for TGV % tic; u_tgv_gpu = TGV_GPU(single(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); +% [ssimval] = ssim(u_tgv_gpu*255,single(Im)*255); +% fprintf('%s %f \n', 'MSSIM error for TGV is:', ssimval); % figure; imshow(u_tgv_gpu, [0 1]); title('TGV denoised image (GPU)'); %% fprintf('Denoise using the ROF-LLT model (CPU) \n'); -lambda_ROF = lambda_reg; % ROF regularisation parameter -lambda_LLT = lambda_reg*0.45; % LLT regularisation parameter -iter_LLT = 1; % iterations +lambda_ROF = 0.016; % ROF regularisation parameter +lambda_LLT = lambda_reg*0.25; % LLT regularisation parameter +iter_LLT = 500; % iterations tau_rof_llt = 0.0025; % time-marching constant tic; u_rof_llt = LLT_ROF(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; rmseROFLLT = (RMSE(u_rof_llt(:),Im(:))); fprintf('%s %f \n', 'RMSE error for TGV is:', rmseROFLLT); +[ssimval] = ssim(u_rof_llt*255,single(Im)*255); +fprintf('%s %f \n', 'MSSIM error for ROFLLT is:', ssimval); figure; imshow(u_rof_llt, [0 1]); title('ROF-LLT denoised image (CPU)'); %% % fprintf('Denoise using the ROF-LLT model (GPU) \n'); -% lambda_ROF = lambda_reg; % ROF regularisation parameter -% lambda_LLT = lambda_reg*0.45; % LLT regularisation parameter +% lambda_ROF = 0.016; % ROF regularisation parameter +% lambda_LLT = lambda_reg*0.25; % LLT regularisation parameter % iter_LLT = 500; % iterations % tau_rof_llt = 0.0025; % time-marching constant % tic; u_rof_llt_g = LLT_ROF_GPU(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; @@ -101,32 +133,16 @@ figure; imshow(u_rof_llt, [0 1]); title('ROF-LLT denoised image (CPU)'); % fprintf('%s %f \n', 'RMSE error for TGV is:', rmseROFLLT_g); % figure; imshow(u_rof_llt_g, [0 1]); title('ROF-LLT denoised image (GPU)'); %% -fprintf('Denoise using Nonlinear-Diffusion model (CPU) \n'); -iter_diff = 800; % number of diffusion iterations -lambda_regDiff = 0.025; % regularisation for the diffusivity -sigmaPar = 0.015; % edge-preserving parameter -tau_param = 0.025; % time-marching constant -tic; u_diff = NonlDiff(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; -rmseDiffus = (RMSE(u_diff(:),Im(:))); -fprintf('%s %f \n', 'RMSE error for Nonlinear Diffusion is:', rmseDiffus); -figure; imshow(u_diff, [0 1]); title('Diffusion denoised image (CPU)'); -%% -% fprintf('Denoise using Nonlinear-Diffusion model (GPU) \n'); -% iter_diff = 800; % number of diffusion iterations -% lambda_regDiff = 0.025; % regularisation for the diffusivity -% sigmaPar = 0.015; % edge-preserving parameter -% tau_param = 0.025; % time-marching constant -% tic; u_diff_g = NonlDiff_GPU(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; -% figure; imshow(u_diff_g, [0 1]); title('Diffusion denoised image (GPU)'); -%% fprintf('Denoise using Fourth-order anisotropic diffusion model (CPU) \n'); iter_diff = 800; % number of diffusion iterations lambda_regDiff = 3.5; % regularisation for the diffusivity -sigmaPar = 0.02; % edge-preserving parameter +sigmaPar = 0.021; % edge-preserving parameter tau_param = 0.0015; % time-marching constant tic; u_diff4 = Diffusion_4thO(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; rmseDiffHO = (RMSE(u_diff4(:),Im(:))); fprintf('%s %f \n', 'RMSE error for Fourth-order anisotropic diffusion is:', rmseDiffHO); +[ssimval] = ssim(u_diff4*255,single(Im)*255); +fprintf('%s %f \n', 'MSSIM error for DIFF4th is:', ssimval); figure; imshow(u_diff4, [0 1]); title('Diffusion 4thO denoised image (CPU)'); %% % fprintf('Denoise using Fourth-order anisotropic diffusion model (GPU) \n'); @@ -146,10 +162,12 @@ tic; [H_i, H_j, Weights] = PatchSelect(single(u0), SearchingWindow, PatchWindow, %% fprintf('Denoise using Non-local Total Variation (CPU) \n'); iter_nltv = 3; % number of nltv iterations -lambda_nltv = 0.05; % regularisation parameter for nltv +lambda_nltv = 0.055; % regularisation parameter for nltv tic; u_nltv = Nonlocal_TV(single(u0), H_i, H_j, 0, Weights, lambda_nltv, iter_nltv); toc; rmse_nltv = (RMSE(u_nltv(:),Im(:))); fprintf('%s %f \n', 'RMSE error for Non-local Total Variation is:', rmse_nltv); +[ssimval] = ssim(u_nltv*255,single(Im)*255); +fprintf('%s %f \n', 'MSSIM error for NLTV is:', ssimval); figure; imagesc(u_nltv, [0 1]); colormap(gray); daspect([1 1 1]); title('Non-local Total Variation denoised image (CPU)'); %% %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< % |