diff options
Diffstat (limited to 'Wrappers/Matlab')
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m | 68 | ||||
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_denoise.m | 20 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m | 22 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m | 22 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/compileGPU_mex.m | 21 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c | 81 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp | 81 |
7 files changed, 293 insertions, 22 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 diff --git a/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m b/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m index 8acc1b7..064b416 100644 --- a/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m +++ b/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m @@ -14,44 +14,60 @@ cd regularisers_CPU Pathmove = sprintf(['..' fsep 'installed' fsep], 1i); -fprintf('%s \n', 'Compiling CPU regularisers...'); +fprintf('%s \n', '<<<<<<<<<<<Compiling CPU regularisers>>>>>>>>>>>>>'); + +fprintf('%s \n', 'Compiling ROF-TV...'); mex ROF_TV.c ROF_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('ROF_TV.mex*',Pathmove); +fprintf('%s \n', 'Compiling FGP-TV...'); mex FGP_TV.c FGP_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('FGP_TV.mex*',Pathmove); +fprintf('%s \n', 'Compiling SB-TV...'); mex SB_TV.c SB_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('SB_TV.mex*',Pathmove); +fprintf('%s \n', 'Compiling dFGP-TV...'); mex FGP_dTV.c FGP_dTV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('FGP_dTV.mex*',Pathmove); +fprintf('%s \n', 'Compiling TNV...'); mex TNV.c TNV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('TNV.mex*',Pathmove); +fprintf('%s \n', 'Compiling NonLinear Diffusion...'); mex NonlDiff.c Diffusion_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('NonlDiff.mex*',Pathmove); +fprintf('%s \n', 'Compiling Anisotropic diffusion of higher order...'); mex Diffusion_4thO.c Diffus4th_order_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('Diffusion_4thO.mex*',Pathmove); +fprintf('%s \n', 'Compiling TGV...'); mex TGV.c TGV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('TGV.mex*',Pathmove); +fprintf('%s \n', 'Compiling ROF-LLT...'); +mex LLT_ROF.c LLT_ROF_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +movefile('LLT_ROF.mex*',Pathmove); + +fprintf('%s \n', 'Compiling additional tools...'); mex TV_energy.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('TV_energy.mex*',Pathmove); %############Inpainters##############% +fprintf('%s \n', 'Compiling Nonlinear/Linear diffusion inpainting...'); mex NonlDiff_Inp.c Diffusion_Inpaint_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('NonlDiff_Inp.mex*',Pathmove); +fprintf('%s \n', 'Compiling Nonlocal marching method for inpaiting...'); mex NonlocalMarching_Inpaint.c NonlocalMarching_Inpaint_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('NonlocalMarching_Inpaint.mex*',Pathmove); -delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* Diffus4th_order_core* TGV_core* CCPiDefines.h +delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* Diffus4th_order_core* TGV_core* LLT_ROF_core* CCPiDefines.h delete Diffusion_Inpaint_core* NonlocalMarching_Inpaint_core* -fprintf('%s \n', 'Regularisers successfully compiled!'); +fprintf('%s \n', '<<<<<<< Regularisers successfully compiled! >>>>>>>'); pathA2 = sprintf(['..' fsep '..' fsep], 1i); cd(pathA2); diff --git a/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m b/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m index ea1ad7d..1b59dc2 100644 --- a/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m +++ b/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m @@ -5,7 +5,7 @@ % not sure if openmp is enabled after the compilation. % Here I present two ways how software can be compiled, if you have some -% other suggestions please contact me at dkazanc@hotmail.com +% other suggestions/remarks please contact me at dkazanc@hotmail.com % >>>>>>>>>>>>>>>>>>>>>>>>>>>>> fsep = '/'; @@ -22,38 +22,54 @@ cd regularisers_CPU Pathmove = sprintf(['..' fsep 'installed' fsep], 1i); -fprintf('%s \n', 'Compiling CPU regularisers...'); +fprintf('%s \n', '<<<<<<<<<<<Compiling CPU regularisers>>>>>>>>>>>>>'); + +fprintf('%s \n', 'Compiling ROF-TV...'); mex ROF_TV.c ROF_TV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" movefile('ROF_TV.mex*',Pathmove); +fprintf('%s \n', 'Compiling FGP-TV...'); mex FGP_TV.c FGP_TV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" movefile('FGP_TV.mex*',Pathmove); +fprintf('%s \n', 'Compiling SB-TV...'); mex SB_TV.c SB_TV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" movefile('SB_TV.mex*',Pathmove); +fprintf('%s \n', 'Compiling dFGP-TV...'); mex FGP_dTV.c FGP_dTV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" movefile('FGP_dTV.mex*',Pathmove); +fprintf('%s \n', 'Compiling TNV...'); mex TNV.c TNV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" movefile('TNV.mex*',Pathmove); +fprintf('%s \n', 'Compiling NonLinear Diffusion...'); mex NonlDiff.c Diffusion_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" movefile('NonlDiff.mex*',Pathmove); +fprintf('%s \n', 'Compiling Anisotropic diffusion of higher order...'); mex Diffusion_4thO.c Diffus4th_order_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" movefile('Diffusion_4thO.mex*',Pathmove); +fprintf('%s \n', 'Compiling TGV...'); mex TGV.c TGV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" movefile('TGV.mex*',Pathmove); +fprintf('%s \n', 'Compiling ROF-LLT...'); +mex LLT_ROF.c LLT_ROF_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" +movefile('LLT_ROF.mex*',Pathmove); + +fprintf('%s \n', 'Compiling additional tools...'); mex TV_energy.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" movefile('TV_energy.mex*',Pathmove); %############Inpainters##############% +fprintf('%s \n', 'Compiling Nonlinear/Linear diffusion inpainting...'); mex NonlDiff_Inp.c Diffusion_Inpaint_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" movefile('NonlDiff_Inp.mex*',Pathmove); +fprintf('%s \n', 'Compiling Nonlocal marching method for inpaiting...'); mex NonlocalMarching_Inpaint.c NonlocalMarching_Inpaint_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99" movefile('NonlocalMarching_Inpaint.mex*',Pathmove); @@ -87,6 +103,8 @@ fprintf('%s \n', 'Regularisers successfully compiled!'); % movefile('Diffusion_4thO.mex*',Pathmove); % mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" TGV.c TGV_core.c utils.c % movefile('TGV.mex*',Pathmove); +% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" LLT_ROF.c LLT_ROF_core.c utils.c +% movefile('LLT_ROF.mex*',Pathmove); % mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" TV_energy.c utils.c % movefile('TV_energy.mex*',Pathmove); % mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" NonlDiff_Inp.c Diffusion_Inpaint_core.c utils.c diff --git a/Wrappers/Matlab/mex_compile/compileGPU_mex.m b/Wrappers/Matlab/mex_compile/compileGPU_mex.m index 003c6ec..e0311ea 100644 --- a/Wrappers/Matlab/mex_compile/compileGPU_mex.m +++ b/Wrappers/Matlab/mex_compile/compileGPU_mex.m @@ -9,8 +9,8 @@ % Tested on Ubuntu 16.04/MATLAB 2016b/cuda7.5/gcc4.9 -% It HAS NOT been tested on Windows, please contact me if you'll be able to -% install software on Windows and I greatefully include it into the release. +% Installation HAS NOT been tested on Windows, please contact me if you'll be able to +% install software on Windows and I gratefully include it into the master release. fsep = '/'; @@ -24,36 +24,49 @@ cd regularisers_GPU Pathmove = sprintf(['..' fsep 'installed' fsep], 1i); -fprintf('%s \n', 'Compiling GPU regularisers (CUDA)...'); +fprintf('%s \n', '<<<<<<<<<<<Compiling GPU regularisers (CUDA)>>>>>>>>>>>>>'); + +fprintf('%s \n', 'Compiling ROF-TV...'); !/usr/local/cuda/bin/nvcc -O0 -c TV_ROF_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/ mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu ROF_TV_GPU.cpp TV_ROF_GPU_core.o movefile('ROF_TV_GPU.mex*',Pathmove); +fprintf('%s \n', 'Compiling FGP-TV...'); !/usr/local/cuda/bin/nvcc -O0 -c TV_FGP_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/ mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu FGP_TV_GPU.cpp TV_FGP_GPU_core.o movefile('FGP_TV_GPU.mex*',Pathmove); +fprintf('%s \n', 'Compiling SB-TV...'); !/usr/local/cuda/bin/nvcc -O0 -c TV_SB_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/ mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu SB_TV_GPU.cpp TV_SB_GPU_core.o movefile('SB_TV_GPU.mex*',Pathmove); +fprintf('%s \n', 'Compiling TGV...'); !/usr/local/cuda/bin/nvcc -O0 -c TGV_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/ mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu TGV_GPU.cpp TGV_GPU_core.o movefile('TGV_GPU.mex*',Pathmove); +fprintf('%s \n', 'Compiling dFGP-TV...'); !/usr/local/cuda/bin/nvcc -O0 -c dTV_FGP_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/ mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu FGP_dTV_GPU.cpp dTV_FGP_GPU_core.o movefile('FGP_dTV_GPU.mex*',Pathmove); +fprintf('%s \n', 'Compiling NonLinear Diffusion...'); !/usr/local/cuda/bin/nvcc -O0 -c NonlDiff_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/ mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu NonlDiff_GPU.cpp NonlDiff_GPU_core.o movefile('NonlDiff_GPU.mex*',Pathmove); +fprintf('%s \n', 'Compiling Anisotropic diffusion of higher order...'); !/usr/local/cuda/bin/nvcc -O0 -c Diffus_4thO_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/ mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu Diffusion_4thO_GPU.cpp Diffus_4thO_GPU_core.o movefile('Diffusion_4thO_GPU.mex*',Pathmove); -delete TV_ROF_GPU_core* TV_FGP_GPU_core* TV_SB_GPU_core* dTV_FGP_GPU_core* NonlDiff_GPU_core* Diffus_4thO_GPU_core* TGV_GPU_core* CCPiDefines.h +fprintf('%s \n', 'Compiling ROF-LLT...'); +!/usr/local/cuda/bin/nvcc -O0 -c LLT_ROF_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/ +mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu LLT_ROF_GPU.cpp LLT_ROF_GPU_core.o +movefile('LLT_ROF_GPU.mex*',Pathmove); + +delete TV_ROF_GPU_core* TV_FGP_GPU_core* TV_SB_GPU_core* dTV_FGP_GPU_core* NonlDiff_GPU_core* Diffus_4thO_GPU_core* TGV_GPU_core* LLT_ROF_GPU_core* CCPiDefines.h fprintf('%s \n', 'All successfully compiled!'); pathA2 = sprintf(['..' fsep '..' fsep], 1i); diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c new file mode 100644 index 0000000..81b717d --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c @@ -0,0 +1,81 @@ +/* + * This work is part of the Core Imaging Library developed by + * Visual Analytics and Imaging System Group of the Science Technology + * Facilities Council, STFC + * + * Copyright 2017 Daniil Kazantsev + * Copyright 2017 Srikanth Nagella, Edoardo Pasca + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * http://www.apache.org/licenses/LICENSE-2.0 + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "matrix.h" +#include "mex.h" +#include "LLT_ROF_core.h" + +/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty. +* +* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. +* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase +* lambdaLLT starting with smaller values. +* +* Input Parameters: +* 1. U0 - original noise image/volume +* 2. lambdaROF - ROF-related regularisation parameter +* 3. lambdaLLT - LLT-related regularisation parameter +* 4. tau - time-marching step +* 5. iter - iterations number (for both models) +* +* Output: +* Filtered/regularised image +* +* References: +* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590. +* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms" +*/ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int number_of_dims, iterationsNumb, dimX, dimY, dimZ; + const int *dim_array; + float *Input, *Output=NULL, lambdaROF, lambdaLLT, tau; + + dim_array = mxGetDimensions(prhs[0]); + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + + if ((nrhs < 3) || (nrhs > 5)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter (ROF), Regularisation parameter (LTT), iterations number, time-marching parameter"); + + /*Handling Matlab input data*/ + Input = (float *) mxGetData(prhs[0]); + lambdaROF = (float) mxGetScalar(prhs[1]); /* ROF regularization parameter */ + lambdaLLT = (float) mxGetScalar(prhs[2]); /* ROF regularization parameter */ + iterationsNumb = 250; + tau = 0.0025; + + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } + if ((nrhs == 4) || (nrhs == 5)) iterationsNumb = (int) mxGetScalar(prhs[3]); /* iterations number */ + if (nrhs == 5) tau = (float) mxGetScalar(prhs[4]); /* marching step parameter */ + + /*Handling Matlab output data*/ + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + + /* output arrays*/ + if (number_of_dims == 2) { + dimZ = 1; /*2D case*/ + /* output image/volume */ + Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + } + if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + + LLT_ROF_CPU_main(Input, Output, lambdaROF, lambdaLLT, iterationsNumb, tau, dimX, dimY, dimZ); +}
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp new file mode 100644 index 0000000..37563b0 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp @@ -0,0 +1,81 @@ +/* + * This work is part of the Core Imaging Library developed by + * Visual Analytics and Imaging System Group of the Science Technology + * Facilities Council, STFC + * + * Copyright 2017 Daniil Kazantsev + * Copyright 2017 Srikanth Nagella, Edoardo Pasca + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * http://www.apache.org/licenses/LICENSE-2.0 + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "matrix.h" +#include "mex.h" +#include "LLT_ROF_GPU_core.h" + +/* CUDA implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty. +* +* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. +* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase +* lambdaLLT starting with smaller values. +* +* Input Parameters: +* 1. U0 - original noise image/volume +* 2. lambdaROF - ROF-related regularisation parameter +* 3. lambdaLLT - LLT-related regularisation parameter +* 4. tau - time-marching step +* 5. iter - iterations number (for both models) +* +* Output: +* Filtered/regularised image +* +* References: +* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590. +* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms" +*/ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int number_of_dims, iterationsNumb, dimX, dimY, dimZ; + const int *dim_array; + float *Input, *Output=NULL, lambdaROF, lambdaLLT, tau; + + dim_array = mxGetDimensions(prhs[0]); + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + + if ((nrhs < 3) || (nrhs > 5)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter (ROF), Regularisation parameter (LTT), iterations number, time-marching parameter"); + + /*Handling Matlab input data*/ + Input = (float *) mxGetData(prhs[0]); + lambdaROF = (float) mxGetScalar(prhs[1]); /* ROF regularization parameter */ + lambdaLLT = (float) mxGetScalar(prhs[2]); /* ROF regularization parameter */ + iterationsNumb = 250; + tau = 0.0025; + + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } + if ((nrhs == 4) || (nrhs == 5)) iterationsNumb = (int) mxGetScalar(prhs[3]); /* iterations number */ + if (nrhs == 5) tau = (float) mxGetScalar(prhs[4]); /* marching step parameter */ + + /*Handling Matlab output data*/ + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + + /* output arrays*/ + if (number_of_dims == 2) { + dimZ = 1; /*2D case*/ + /* output image/volume */ + Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + } + if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + + LLT_ROF_GPU_main(Input, Output, lambdaROF, lambdaLLT, iterationsNumb, tau, dimX, dimY, dimZ); +}
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