diff options
Diffstat (limited to 'demos/demoMatlab_3Ddenoise.m')
| -rw-r--r-- | demos/demoMatlab_3Ddenoise.m | 35 |
1 files changed, 20 insertions, 15 deletions
diff --git a/demos/demoMatlab_3Ddenoise.m b/demos/demoMatlab_3Ddenoise.m index cf2c88a..ec0fd88 100644 --- a/demos/demoMatlab_3Ddenoise.m +++ b/demos/demoMatlab_3Ddenoise.m @@ -23,7 +23,8 @@ lambda_reg = 0.03; % regularsation parameter for all methods fprintf('Denoise a volume using the ROF-TV model (CPU) \n'); 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; +epsil_tol = 0.0; % tolerance +tic; [u_rof,infovec] = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof, epsil_tol); toc; energyfunc_val_rof = TV_energy(single(u_rof),single(vol3D),lambda_reg, 1); % get energy function value rmse_rof = (RMSE(Ideal3D(:),u_rof(:))); fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rof); @@ -39,8 +40,8 @@ figure; imshow(u_rof(:,:,7), [0 1]); title('ROF-TV denoised volume (CPU)'); %% 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; +epsil_tol = 0.0; % tolerance +tic; [u_fgp,infovec] = 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 rmse_fgp = (RMSE(Ideal3D(:),u_fgp(:))); fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgp); @@ -56,8 +57,8 @@ figure; imshow(u_fgp(:,:,7), [0 1]); title('FGP-TV denoised volume (CPU)'); %% 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; +epsil_tol = 0.0; % tolerance +tic; [u_sb,infovec] = 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 rmse_sb = (RMSE(Ideal3D(:),u_sb(:))); fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sb); @@ -76,7 +77,8 @@ 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; +epsil_tol = 0.0; % tolerance +tic; [u_rof_llt, infovec] = LLT_ROF(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt, epsil_tol); 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)'); @@ -86,7 +88,7 @@ figure; imshow(u_rof_llt(:,:,7), [0 1]); title('ROF-LLT denoised volume (CPU)'); % 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; +% tic; u_rof_llt_g = LLT_ROF_GPU(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt, epsil_tol); 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)'); @@ -96,7 +98,8 @@ iter_diff = 300; % 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(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; +epsil_tol = 0.0; % tolerance +tic; [u_diff, infovec] = NonlDiff(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber', epsil_tol); toc; 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)'); @@ -106,7 +109,7 @@ figure; imshow(u_diff(:,:,7), [0 1]); title('Diffusion denoised volume (CPU)'); % 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(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; +% tic; u_diff_g = NonlDiff_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber', epsil_tol); toc; % 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)'); @@ -116,7 +119,8 @@ iter_diff = 300; % 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 = Diffusion_4thO(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; +epsil_tol = 0.0; % tolerance +tic; u_diff4 = Diffusion_4thO(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, epsil_tol); toc; 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)'); @@ -126,7 +130,7 @@ figure; imshow(u_diff4(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (CP % 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(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; +% tic; u_diff4_g = Diffusion_4thO_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, epsil_tol); toc; % 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)'); @@ -136,7 +140,8 @@ lambda_TGV = 0.03; % regularisation parameter alpha1 = 1.0; % parameter to control the first-order term alpha0 = 2.0; % parameter to control the second-order term iter_TGV = 500; % number of Primal-Dual iterations for TGV -tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV); toc; +epsil_tol = 0.0; % tolerance +tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, epsil_tol); toc; rmseTGV = RMSE(Ideal3D(:),u_tgv(:)); fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV); figure; imshow(u_tgv(:,:,3), [0 1]); title('TGV denoised volume (CPU)'); @@ -146,7 +151,7 @@ figure; imshow(u_tgv(:,:,3), [0 1]); title('TGV denoised volume (CPU)'); % alpha1 = 1.0; % parameter to control the first-order term % alpha0 = 2.0; % parameter to control the second-order term % iter_TGV = 500; % number of Primal-Dual iterations for TGV -% tic; u_tgv_gpu = TGV_GPU(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV); toc; +% tic; u_tgv_gpu = TGV_GPU(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, epsil_tol); toc; % rmseTGV = RMSE(Ideal3D(:),u_tgv_gpu(:)); % fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV); % figure; imshow(u_tgv_gpu(:,:,3), [0 1]); title('TGV denoised volume (GPU)'); @@ -163,7 +168,7 @@ vol3D_ref(vol3D_ref < 0) = 0; % vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV) iter_fgp = 300; % number of FGP iterations -epsil_tol = 1.0e-05; % tolerance +epsil_tol = 0.0; % 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(:,:,7), [0 1]); title('FGP-dTV denoised volume (CPU)'); @@ -179,7 +184,7 @@ vol3D_ref(vol3D_ref < 0) = 0; % vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV) iter_fgp = 300; % number of FGP iterations -epsil_tol = 1.0e-05; % tolerance +epsil_tol = 0.0; % 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(:,:,7), [0 1]); title('FGP-dTV denoised volume (GPU)'); 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