diff options
Diffstat (limited to 'Wrappers/Matlab/demos')
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m | 68 | ||||
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_denoise.m | 20 |
2 files changed, 75 insertions, 13 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m index 9a65e37..5cc47b3 100644 --- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m @@ -6,11 +6,13 @@ addpath(Path1); addpath(Path2); N = 512; -slices = 30; +slices = 15; vol3D = zeros(N,N,slices, 'single'); +Ideal3D = zeros(N,N,slices, 'single'); Im = double(imread('lena_gray_512.tif'))/255; % loading image for i = 1:slices vol3D(:,:,i) = Im + .05*randn(size(Im)); +Ideal3D(:,:,i) = Im; end vol3D(vol3D < 0) = 0; figure; imshow(vol3D(:,:,15), [0 1]); title('Noisy image'); @@ -23,39 +25,71 @@ 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, 1); % get energy function value -figure; imshow(u_rof(:,:,15), [0 1]); title('ROF-TV denoised volume (CPU)'); +rmse_rof = (RMSE(Ideal3D(:),u_rof(:))); +fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rof); +figure; imshow(u_rof(:,:,7), [0 1]); title('ROF-TV denoised volume (CPU)'); %% % fprintf('Denoise a volume using the ROF-TV model (GPU) \n'); % tau_rof = 0.0025; % time-marching constant % iter_rof = 300; % number of ROF iterations % 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)'); +% rmse_rofG = (RMSE(Ideal3D(:),u_rofG(:))); +% fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rofG); +% figure; imshow(u_rofG(:,:,7), [0 1]); title('ROF-TV denoised volume (GPU)'); %% 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, 1); % get energy function value -figure; imshow(u_fgp(:,:,15), [0 1]); title('FGP-TV denoised volume (CPU)'); +rmse_fgp = (RMSE(Ideal3D(:),u_fgp(:))); +fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgp); +figure; imshow(u_fgp(:,:,7), [0 1]); title('FGP-TV denoised volume (CPU)'); %% % fprintf('Denoise a volume using the FGP-TV model (GPU) \n'); % iter_fgp = 300; % number of FGP iterations % epsil_tol = 1.0e-05; % tolerance % 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)'); +% rmse_fgpG = (RMSE(Ideal3D(:),u_fgpG(:))); +% fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgpG); +% figure; imshow(u_fgpG(:,:,7), [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; 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)'); +rmse_sb = (RMSE(Ideal3D(:),u_sb(:))); +fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sb); +figure; imshow(u_sb(:,:,7), [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)'); +% rmse_sbG = (RMSE(Ideal3D(:),u_sbG(:))); +% fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sbG); +% figure; imshow(u_sbG(:,:,7), [0 1]); title('SB-TV denoised volume (GPU)'); +%% +fprintf('Denoise a volume using the ROF-LLT model (CPU) \n'); +lambda_ROF = lambda_reg; % ROF regularisation parameter +lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter +iter_LLT = 300; % iterations +tau_rof_llt = 0.0025; % time-marching constant +tic; u_rof_llt = LLT_ROF(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; +rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt(:))); +fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt); +figure; imshow(u_rof_llt(:,:,7), [0 1]); title('ROF-LLT denoised volume (CPU)'); +%% +% fprintf('Denoise a volume using the ROF-LLT model (GPU) \n'); +% lambda_ROF = lambda_reg; % ROF regularisation parameter +% lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter +% iter_LLT = 300; % iterations +% tau_rof_llt = 0.0025; % time-marching constant +% tic; u_rof_llt_g = LLT_ROF_GPU(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; +% rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt_g(:))); +% fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt); +% figure; imshow(u_rof_llt_g(:,:,7), [0 1]); title('ROF-LLT denoised volume (GPU)'); %% fprintf('Denoise a volume using Nonlinear-Diffusion model (CPU) \n'); iter_diff = 300; % number of diffusion iterations @@ -63,7 +97,9 @@ 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(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; -figure; imshow(u_diff(:,:,15), [0 1]); title('Diffusion denoised volume (CPU)'); +rmse_diff = (RMSE(Ideal3D(:),u_diff(:))); +fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff); +figure; imshow(u_diff(:,:,7), [0 1]); title('Diffusion denoised volume (CPU)'); %% % fprintf('Denoise a volume using Nonlinear-Diffusion model (GPU) \n'); % iter_diff = 300; % number of diffusion iterations @@ -71,7 +107,9 @@ figure; imshow(u_diff(:,:,15), [0 1]); title('Diffusion denoised volume (CPU)'); % sigmaPar = 0.015; % edge-preserving parameter % tau_param = 0.025; % time-marching constant % tic; u_diff_g = NonlDiff_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; -% figure; imshow(u_diff_g(:,:,15), [0 1]); title('Diffusion denoised volume (GPU)'); +% rmse_diff = (RMSE(Ideal3D(:),u_diff_g(:))); +% fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff); +% figure; imshow(u_diff_g(:,:,7), [0 1]); title('Diffusion denoised volume (GPU)'); %% fprintf('Denoise using Fourth-order anisotropic diffusion model (CPU) \n'); iter_diff = 300; % number of diffusion iterations @@ -79,7 +117,9 @@ lambda_regDiff = 3.5; % regularisation for the diffusivity sigmaPar = 0.02; % edge-preserving parameter tau_param = 0.0015; % time-marching constant tic; u_diff4 = Diffusion_4thO(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; -figure; imshow(u_diff4(:,:,15), [0 1]); title('Diffusion 4thO denoised volume (CPU)'); +rmse_diff4 = (RMSE(Ideal3D(:),u_diff4(:))); +fprintf('%s %f \n', 'RMSE error for Anis.Diff of 4th order is:', rmse_diff4); +figure; imshow(u_diff4(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (CPU)'); %% % fprintf('Denoise using Fourth-order anisotropic diffusion model (GPU) \n'); % iter_diff = 300; % number of diffusion iterations @@ -87,7 +127,9 @@ figure; imshow(u_diff4(:,:,15), [0 1]); title('Diffusion 4thO denoised volume (C % sigmaPar = 0.02; % edge-preserving parameter % tau_param = 0.0015; % time-marching constant % tic; u_diff4_g = Diffusion_4thO_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; -% figure; imshow(u_diff4_g(:,:,15), [0 1]); title('Diffusion 4thO denoised volume (GPU)'); +% rmse_diff4 = (RMSE(Ideal3D(:),u_diff4_g(:))); +% fprintf('%s %f \n', 'RMSE error for Anis.Diff of 4th order is:', rmse_diff4); +% figure; imshow(u_diff4_g(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (GPU)'); %% %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< % @@ -105,7 +147,7 @@ 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_reg, iter_fgp, epsil_tol, eta); toc; -figure; imshow(u_fgp_dtv(:,:,15), [0 1]); title('FGP-dTV denoised volume (CPU)'); +figure; imshow(u_fgp_dtv(:,:,7), [0 1]); title('FGP-dTV denoised volume (CPU)'); %% fprintf('Denoise a volume using the FGP-dTV model (GPU) \n'); @@ -121,5 +163,5 @@ 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_reg, iter_fgp, epsil_tol, eta); toc; -figure; imshow(u_fgp_dtv_g(:,:,15), [0 1]); title('FGP-dTV denoised volume (GPU)'); +figure; imshow(u_fgp_dtv_g(:,:,7), [0 1]); title('FGP-dTV denoised volume (GPU)'); %% diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m index 3f0ca54..d11bc63 100644 --- a/Wrappers/Matlab/demos/demoMatlab_denoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m @@ -79,6 +79,26 @@ figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)'); % fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV_gpu); % 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 +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); +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 +% 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); +% 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 |