diff options
Diffstat (limited to 'demos/demoMatlab_denoise.m')
| -rw-r--r-- | demos/demoMatlab_denoise.m | 36 |
1 files changed, 19 insertions, 17 deletions
diff --git a/demos/demoMatlab_denoise.m b/demos/demoMatlab_denoise.m index a22b40a..7581068 100644 --- a/demos/demoMatlab_denoise.m +++ b/demos/demoMatlab_denoise.m @@ -19,7 +19,7 @@ iter_rof = 2000; % number of ROF iterations tau_rof = 0.001; % time-marching constant epsil_tol = 0.0; % tolerance tic; [u_rof,infovec] = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof, epsil_tol); toc; -%energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg, 1); % get energy function value +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); @@ -27,7 +27,7 @@ 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'); -% tic; u_rofG = ROF_TV_GPU(single(u0), lambda_reg, iter_rof, tau_rof, epsil_tol); toc; +% tic; [u_rofG,infovec] = ROF_TV_GPU(single(u0), lambda_reg, iter_rof, tau_rof, epsil_tol); toc; % figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)'); %% fprintf('Denoise using the FGP-TV model (CPU) \n'); @@ -59,8 +59,6 @@ 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 = 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)'); %% @@ -69,7 +67,8 @@ 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; +epsil_tol = 1.0e-06; % tolerance +tic; [u_diff,infovec] = NonlDiff(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber', epsil_tol); 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); @@ -85,11 +84,11 @@ figure; imshow(u_diff, [0 1]); title('Diffusion denoised image (CPU)'); % figure; imshow(u_diff_g, [0 1]); title('Diffusion denoised image (GPU)'); %% fprintf('Denoise using the TGV model (CPU) \n'); -lambda_TGV = 0.034; % regularisation parameter +lambda_TGV = 0.045; % regularisation parameter alpha1 = 1.0; % parameter to control the first-order term -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; +alpha0 = 2.0; % parameter to control the second-order term +iter_TGV = 2500; % number of Primal-Dual iterations for TGV +tic; [u_tgv,infovec] = 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); @@ -109,11 +108,12 @@ figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)'); % figure; imshow(u_tgv_gpu, [0 1]); title('TGV denoised image (GPU)'); %% fprintf('Denoise using the ROF-LLT model (CPU) \n'); -lambda_ROF = 0.016; % ROF regularisation parameter -lambda_LLT = lambda_reg*0.25; % LLT regularisation parameter -iter_LLT = 500; % iterations +lambda_ROF = 0.02; % ROF regularisation parameter +lambda_LLT = 0.01; % LLT regularisation parameter +iter_LLT = 1000; % 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; +epsil_tol = 1.0e-06; % tolerance +tic; [u_rof_llt,infovec] = LLT_ROF(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt,epsil_tol); 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); @@ -132,10 +132,11 @@ figure; imshow(u_rof_llt, [0 1]); title('ROF-LLT denoised image (CPU)'); %% 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 = 0.021; % edge-preserving parameter +lambda_regDiff = 2.5; % regularisation for the diffusivity +sigmaPar = 0.03; % 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; +epsil_tol = 1.0e-06; % tolerance +tic; [u_diff4,infovec] = Diffusion_4thO(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, epsil_tol); 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); @@ -174,10 +175,11 @@ fprintf('Denoise using the FGP-dTV model (CPU) \n'); 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_reg = 0.04; 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_reg, iter_fgp, epsil_tol, eta); toc; +tic; [u_fgp_dtv,infovec] = FGP_dTV(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; rmse_dTV= (RMSE(u_fgp_dtv(:),Im(:))); fprintf('%s %f \n', 'RMSE error for Directional Total Variation (dTV) is:', rmse_dTV); figure; imshow(u_fgp_dtv, [0 1]); title('FGP-dTV denoised image (CPU)'); |