diff options
Diffstat (limited to 'demos/demoMatlab_denoise.m')
| -rw-r--r-- | demos/demoMatlab_denoise.m | 119 |
1 files changed, 49 insertions, 70 deletions
diff --git a/demos/demoMatlab_denoise.m b/demos/demoMatlab_denoise.m index 5e92ee1..9d89138 100644 --- a/demos/demoMatlab_denoise.m +++ b/demos/demoMatlab_denoise.m @@ -3,8 +3,8 @@ clear; close all fsep = '/'; Path1 = sprintf(['..' fsep 'src' fsep 'Matlab' fsep 'mex_compile' fsep 'installed'], 1i); -Path2 = sprintf([ data' fsep], 1i); -Path3 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'supp'], 1i); +Path2 = sprintf(['data' fsep], 1i); +Path3 = sprintf(['..' fsep 'src' fsep 'Matlab' fsep 'supp'], 1i); addpath(Path1); addpath(Path2); addpath(Path3); @@ -12,13 +12,13 @@ addpath(Path3); 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'); - %% 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 = 1200; % number of ROF iterations -tic; u_rof = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof); toc; +lambda_reg = 0.03; % regularsation parameter for all methods +iter_rof = 2000; % number of ROF iterations +tau_rof = 0.01; % time-marching constant +epsil_tol = 0.0; % tolerance / 1.0e-06 +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 rmseROF = (RMSE(u_rof(:),Im(:))); fprintf('%s %f \n', 'RMSE error for ROF-TV is:', rmseROF); @@ -26,17 +26,15 @@ fprintf('%s %f \n', 'RMSE error for ROF-TV is:', rmseROF); 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 = 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 ROF-TV model (GPU) \n'); +%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'); -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; +lambda_reg = 0.03; +iter_fgp = 500; % number of FGP iterations +epsil_tol = 0.0; % tolerance +tic; [u_fgp,infovec] = 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); @@ -45,15 +43,14 @@ 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 = 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 = 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; +lambda_reg = 0.03; +iter_sb = 200; % number of SB iterations +epsil_tol = 0.0; % tolerance +tic; [u_sb,infovec] = 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); @@ -62,8 +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)'); %% @@ -72,51 +67,43 @@ 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 = 0.0; % 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); 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 Nonlinear-Diffusion model (GPU) \n'); +%tic; u_diff_g = NonlDiff_GPU(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber', epsil_tol); toc; +%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 = 0.035; % 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 +L2 = 12.0; % convergence parameter +iter_TGV = 1200; % number of Primal-Dual iterations for TGV +epsil_tol = 0.0; % tolerance +tic; [u_tgv,infovec] = TGV(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV, L2, epsil_tol); toc; +figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)'); 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 = 0.034; % 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_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); +% tic; u_tgv_gpu = TGV_GPU(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV, L2, epsil_tol); toc; % 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 -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; +lambda_ROF = 0.02; % ROF regularisation parameter +lambda_LLT = 0.015; % LLT regularisation parameter +iter_LLT = 2000; % iterations +tau_rof_llt = 0.01; % time-marching constant +epsil_tol = 0.0; % 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); @@ -124,34 +111,25 @@ 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 = 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; -% rmseROFLLT_g = (RMSE(u_rof_llt_g(:),Im(:))); -% fprintf('%s %f \n', 'RMSE error for TGV is:', rmseROFLLT_g); +% tic; u_rof_llt_g = LLT_ROF_GPU(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt, epsil_tol); toc; % figure; imshow(u_rof_llt_g, [0 1]); title('ROF-LLT 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 = 0.021; % 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; +lambda_regDiff = 3; % regularisation for the diffusivity +sigmaPar = 0.03; % edge-preserving parameter +tau_param = 0.0025; % time-marching constant +epsil_tol = 0.0; % 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); 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'); -% iter_diff = 800; % number of diffusion iterations -% lambda_regDiff = 3.5; % regularisation for the diffusivity -% sigmaPar = 0.02; % edge-preserving parameter -% tau_param = 0.0015; % time-marching constant -% tic; u_diff4_g = Diffusion_4thO_GPU(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; -% figure; imshow(u_diff4_g, [0 1]); title('Diffusion 4thO denoised image (GPU)'); +%fprintf('Denoise using Fourth-order anisotropic diffusion model (GPU) \n'); +%tic; u_diff4_g = Diffusion_4thO_GPU(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; +%figure; imshow(u_diff4_g, [0 1]); title('Diffusion 4thO denoised image (GPU)'); %% fprintf('Weights pre-calculation for Non-local TV (takes time on CPU) \n'); SearchingWindow = 7; @@ -177,10 +155,11 @@ 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 +epsil_tol = 0.0; % 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)'); |