diff options
Diffstat (limited to 'demos')
| -rw-r--r-- | demos/SoftwareX_supp/Demo_RealData_Recon_SX.py | 4 | ||||
| -rw-r--r-- | demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py | 2 | ||||
| -rw-r--r-- | demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py | 3 | ||||
| -rw-r--r-- | demos/demoMatlab_3Ddenoise.m | 35 | ||||
| -rw-r--r-- | demos/demoMatlab_denoise.m | 83 |
5 files changed, 55 insertions, 72 deletions
diff --git a/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py b/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py index 01491d9..ca8f1d2 100644 --- a/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py +++ b/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py @@ -111,7 +111,7 @@ RectoolsIR = RecToolsIR(DetectorsDimH = np.size(det_y_crop), # DetectorsDimH # datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip) nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE') OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets - tolerance = 1e-08, # tolerance to stop outer iterations earlier + tolerance = 0.0, # tolerance to stop inner (regularisation) iterations earlier device='gpu') #%% print ("Reconstructing with ADMM method using SB-TV penalty") @@ -228,4 +228,4 @@ for i in range(0,np.size(RecADMM_reg_tgv,0)): # Saving recpnstructed data with a unique time label np.save('Dendr_ADMM_TGV'+str(time_label)+'.npy', RecADMM_reg_tgv) del RecADMM_reg_tgv -#%%
\ No newline at end of file +#%% diff --git a/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py b/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py index 59ffc0e..be99afe 100644 --- a/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py +++ b/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py @@ -77,7 +77,7 @@ RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det, # DetectorsDimH # detector d datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip) nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE') OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets - tolerance = 1e-08, # tolerance to stop outer iterations earlier + tolerance = 0.0, # tolerance to stop inner (regularisation) iterations earlier device='gpu') #%% param_space = 30 diff --git a/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py b/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py index 99b9fe8..ae2bfba 100644 --- a/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py +++ b/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py @@ -78,7 +78,6 @@ plt.title('3D Phantom, coronal (Y-Z) view') plt.subplot(133) plt.imshow(phantom[:,:,sliceSel],vmin=0, vmax=1, cmap="PuOr") plt.title('3D Phantom, sagittal view') - """ plt.show() #%% @@ -164,7 +163,7 @@ RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det, # DetectorsDimH # detector d datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip) nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE') OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets - tolerance = 1e-08, # tolerance to stop inner iterations earlier + tolerance = 0.0, # tolerance to stop inner (regularisation) iterations earlier device='gpu') #%% print ("Reconstructing with ADMM method using SB-TV penalty") 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)'); diff --git a/demos/demoMatlab_denoise.m b/demos/demoMatlab_denoise.m index 7581068..377a447 100644 --- a/demos/demoMatlab_denoise.m +++ b/demos/demoMatlab_denoise.m @@ -14,10 +14,10 @@ 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 = 0.02; % regularsation parameter for all methods +lambda_reg = 0.03; % regularsation parameter for all methods iter_rof = 2000; % number of ROF iterations -tau_rof = 0.001; % time-marching constant -epsil_tol = 0.0; % tolerance +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(:))); @@ -26,14 +26,14 @@ 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'); -% 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 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 = 0.02; +lambda_reg = 0.03; iter_fgp = 500; % number of FGP iterations -epsil_tol = 1.0e-06; % tolerance +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(:))); @@ -48,8 +48,8 @@ figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)'); %% fprintf('Denoise using the SB-TV model (CPU) \n'); lambda_reg = 0.03; -iter_sb = 300; % number of SB iterations -epsil_tol = 1.0e-06; % tolerance +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(:))); @@ -67,7 +67,7 @@ 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 -epsil_tol = 1.0e-06; % tolerance +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); @@ -75,20 +75,17 @@ fprintf('%s %f \n', 'RMSE error for Nonlinear Diffusion is:', rmseDiffus); 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 = 0.045; % regularisation parameter +lambda_TGV = 0.035; % regularisation parameter alpha1 = 1.0; % parameter to control the first-order term 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; +iter_TGV = 20; % 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, epsil_tol); toc; rmseTGV = (RMSE(u_tgv(:),Im(:))); fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV); [ssimval] = ssim(u_tgv*255,single(Im)*255); @@ -96,23 +93,15 @@ 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, 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 = 0.02; % ROF regularisation parameter -lambda_LLT = 0.01; % LLT regularisation parameter -iter_LLT = 1000; % iterations -tau_rof_llt = 0.0025; % time-marching constant -epsil_tol = 1.0e-06; % tolerance +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); @@ -121,21 +110,15 @@ 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 = 2.5; % regularisation for the diffusivity +lambda_regDiff = 3; % regularisation for the diffusivity sigmaPar = 0.03; % edge-preserving parameter -tau_param = 0.0015; % time-marching constant -epsil_tol = 1.0e-06; % tolerance +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); @@ -143,13 +126,9 @@ fprintf('%s %f \n', 'RMSE error for Fourth-order anisotropic diffusion is:', rms 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; @@ -177,7 +156,7 @@ u_ref = Im + .01*randn(size(Im)); u_ref(u_ref < 0) = 0; 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,infovec] = FGP_dTV(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; rmse_dTV= (RMSE(u_fgp_dtv(:),Im(:))); |