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| author | Daniil Kazantsev <dkazanc@hotmail.com> | 2018-01-24 17:39:38 +0000 |
|---|---|---|
| committer | Edoardo Pasca <edo.paskino@gmail.com> | 2018-01-25 11:21:12 +0000 |
| commit | 723a2d3fbe9a7a8c145b5f5ef481dcd4a3799383 (patch) | |
| tree | b4351067e39021973b7f155a04cd967289ac9ddc /Wrappers/Matlab | |
| parent | 9ff389298a1dc4d94222cfcc6e9c6c945401af03 (diff) | |
| download | regularization-723a2d3fbe9a7a8c145b5f5ef481dcd4a3799383.tar.gz regularization-723a2d3fbe9a7a8c145b5f5ef481dcd4a3799383.tar.bz2 regularization-723a2d3fbe9a7a8c145b5f5ef481dcd4a3799383.tar.xz regularization-723a2d3fbe9a7a8c145b5f5ef481dcd4a3799383.zip | |
all Matlab related stuff have been moved to wrappers
Diffstat (limited to 'Wrappers/Matlab')
34 files changed, 3852 insertions, 11 deletions
diff --git a/Wrappers/Matlab/compile_mex.m b/Wrappers/Matlab/compile_mex.m deleted file mode 100644 index 66c05da..0000000 --- a/Wrappers/Matlab/compile_mex.m +++ /dev/null @@ -1,11 +0,0 @@ -% compile mex's in Matlab once -cd regularizers_CPU/ - -mex LLT_model.c LLT_model_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" -mex FGP_TV.c FGP_TV_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" -mex SplitBregman_TV.c SplitBregman_TV_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" -mex TGV_PD.c TGV_PD_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" -mex PatchBased_Regul.c PatchBased_Regul_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" - -cd ../../ -cd demos diff --git a/Wrappers/Matlab/demos/Demo_Phantom3D_Cone.m b/Wrappers/Matlab/demos/Demo_Phantom3D_Cone.m new file mode 100644 index 0000000..a8f2c92 --- /dev/null +++ b/Wrappers/Matlab/demos/Demo_Phantom3D_Cone.m @@ -0,0 +1,67 @@ +% A demo script to reconstruct 3D synthetic data using FISTA method for +% CONE BEAM geometry +% requirements: ASTRA-toolbox and TomoPhantom toolbox + +close all;clc;clear all; +% adding paths +addpath('../data/'); +addpath('../main_func/'); addpath('../main_func/regularizers_CPU/'); addpath('../main_func/regularizers_GPU/NL_Regul/'); addpath('../main_func/regularizers_GPU/Diffus_HO/'); +addpath('../supp/'); + +%% +% build 3D phantom using TomoPhantom +modelNo = 3; % see Phantom3DLibrary.dat file in TomoPhantom +N = 256; % x-y-z size (cubic image) +angles = 0:1.5:360; % angles vector in degrees +angles_rad = angles*(pi/180); % conversion to radians +det_size = round(sqrt(2)*N); % detector size + +%---------TomoPhantom routines---------% +pathTP = '/home/algol/Documents/MATLAB/TomoPhantom/functions/models/Phantom3DLibrary.dat'; % path to TomoPhantom parameters file +TomoPhantom = buildPhantom3D(modelNo,N,pathTP); % generate 3D phantom +%--------------------------------------% +%% +% using ASTRA-toolbox to set the projection geometry (cone beam) +% eg: astra.create_proj_geom('cone', 1.0 (resol), 1.0 (resol), detectorRowCount, detectorColCount, angles, originToSource, originToDetector) +vol_geom = astra_create_vol_geom(N,N,N); +proj_geom = astra_create_proj_geom('cone', 1.0, 1.0, N, det_size, angles_rad, 2000, 2160); +%% +% do forward projection using ASTRA +% inverse crime data generation +[sino_id, SinoCone3D] = astra_create_sino3d_cuda(TomoPhantom, proj_geom, vol_geom); +astra_mex_data3d('delete', sino_id); +%% +fprintf('%s\n', 'Reconstructing with CGLS using ASTRA-toolbox ...'); +vol_id = astra_mex_data3d('create', '-vol', vol_geom, 0); +proj_id = astra_mex_data3d('create', '-proj3d', proj_geom, SinoCone3D); +cfg = astra_struct('CGLS3D_CUDA'); +cfg.ProjectionDataId = proj_id; +cfg.ReconstructionDataId = vol_id; +cfg.option.MinConstraint = 0; +alg_id = astra_mex_algorithm('create', cfg); +astra_mex_algorithm('iterate', alg_id, 15); +reconASTRA_3D = astra_mex_data3d('get', vol_id); +%% +fprintf('%s\n', 'Reconstruction using FISTA-LS without regularization...'); +clear params +% define parameters +params.proj_geom = proj_geom; % pass geometry to the function +params.vol_geom = vol_geom; +params.sino = single(SinoCone3D); % sinogram +params.iterFISTA = 30; %max number of outer iterations +params.X_ideal = TomoPhantom; % ideal phantom +params.show = 1; % visualize reconstruction on each iteration +params.slice = round(N/2); params.maxvalplot = 1; +tic; [X_FISTA, output] = FISTA_REC(params); toc; + +error_FISTA = output.Resid_error; obj_FISTA = output.objective; +fprintf('%s %.4f\n', 'Min RMSE for FISTA-LS reconstruction is:', min(error_FISTA(:))); + +Resid3D = (TomoPhantom - X_FISTA).^2; +figure(2); +subplot(1,2,1); imshow(X_FISTA(:,:,params.slice),[0 params.maxvalplot]); title('FISTA-LS reconstruction'); colorbar; +subplot(1,2,2); imshow(Resid3D(:,:,params.slice),[0 0.1]); title('residual'); colorbar; +figure(3); +subplot(1,2,1); plot(error_FISTA); title('RMSE plot'); colorbar; +subplot(1,2,2); plot(obj_FISTA); title('Objective plot'); colorbar; +%%
\ No newline at end of file diff --git a/Wrappers/Matlab/demos/Demo_Phantom3D_Parallel.m b/Wrappers/Matlab/demos/Demo_Phantom3D_Parallel.m new file mode 100644 index 0000000..4219bd1 --- /dev/null +++ b/Wrappers/Matlab/demos/Demo_Phantom3D_Parallel.m @@ -0,0 +1,121 @@ +% A demo script to reconstruct 3D synthetic data using FISTA method for
+% PARALLEL BEAM geometry
+% requirements: ASTRA-toolbox and TomoPhantom toolbox
+
+close all;clc;clear;
+% adding paths
+addpath('../data/');
+addpath('../main_func/'); addpath('../main_func/regularizers_CPU/'); addpath('../main_func/regularizers_GPU/NL_Regul/'); addpath('../main_func/regularizers_GPU/Diffus_HO/');
+addpath('../supp/');
+
+%%
+% Main reconstruction/data generation parameters
+modelNo = 2; % see Phantom3DLibrary.dat file in TomoPhantom
+N = 256; % x-y-z size (cubic image)
+angles = 1:0.5:180; % angles vector in degrees
+angles_rad = angles*(pi/180); % conversion to radians
+det_size = round(sqrt(2)*N); % detector size
+
+%---------TomoPhantom routines---------%
+pathTP = '/home/algol/Documents/MATLAB/TomoPhantom/functions/models/Phantom3DLibrary.dat'; % path to TomoPhantom parameters file
+TomoPhantom = buildPhantom3D(modelNo,N,pathTP); % generate 3D phantom
+sino_tomophan3D = buildSino3D(modelNo, N, det_size, single(angles),pathTP); % generate ideal data
+%--------------------------------------%
+% Adding noise and distortions if required
+sino_tomophan3D = sino_add_artifacts(sino_tomophan3D,'rings');
+% adding Poisson noise
+dose = 3e9; % photon flux (controls noise level)
+multifactor = max(sino_tomophan3D(:));
+dataExp = dose.*exp(-sino_tomophan3D/multifactor); % noiseless raw data
+dataRaw = astra_add_noise_to_sino(dataExp, dose); % pre-log noisy raw data (weights)
+sino3D_log = log(dose./max(dataRaw,1))*multifactor; %log corrected data -> sinogram
+clear dataExp sino_tomophan3D
+%
+%%
+%-------------Astra toolbox------------%
+% one can generate data using ASTRA toolbox
+proj_geom = astra_create_proj_geom('parallel', 1, det_size, angles_rad);
+vol_geom = astra_create_vol_geom(N,N);
+sino_ASTRA3D = zeros(det_size, length(angles), N, 'single');
+for i = 1:N
+[sino_id, sinoT] = astra_create_sino_cuda(TomoPhantom(:,:,i), proj_geom, vol_geom);
+sino_ASTRA3D(:,:,i) = sinoT';
+astra_mex_data2d('delete', sino_id);
+end
+%--------------------------------------%
+%%
+% using ASTRA-toolbox to set the projection geometry (parallel beam)
+proj_geom = astra_create_proj_geom('parallel', 1, det_size, angles_rad);
+vol_geom = astra_create_vol_geom(N,N);
+%%
+fprintf('%s\n', 'Reconstructing with FBP using ASTRA-toolbox ...');
+reconASTRA_3D = zeros(size(TomoPhantom),'single');
+for k = 1:N
+vol_id = astra_mex_data2d('create', '-vol', vol_geom, 0);
+proj_id = astra_mex_data2d('create', '-sino', proj_geom, sino3D_log(:,:,k)');
+cfg = astra_struct('FBP_CUDA');
+cfg.ProjectionDataId = proj_id;
+cfg.ReconstructionDataId = vol_id;
+cfg.option.MinConstraint = 0;
+alg_id = astra_mex_algorithm('create', cfg);
+astra_mex_algorithm('iterate', alg_id, 1);
+rec = astra_mex_data2d('get', vol_id);
+reconASTRA_3D(:,:,k) = single(rec);
+end
+figure; imshow(reconASTRA_3D(:,:,128), [0 1.3]);
+%%
+%%
+fprintf('%s\n', 'Reconstruction using OS-FISTA-PWLS without regularization...');
+clear params
+% define parameters
+params.proj_geom = proj_geom; % pass geometry to the function
+params.vol_geom = vol_geom;
+params.sino = single(sino3D_log); % sinogram
+params.iterFISTA = 15; %max number of outer iterations
+params.X_ideal = TomoPhantom; % ideal phantom
+params.weights = dataRaw./max(dataRaw(:)); % statistical weight for PWLS
+params.subsets = 12; % the number of subsets
+params.show = 1; % visualize reconstruction on each iteration
+params.slice = 128; params.maxvalplot = 1.3;
+tic; [X_FISTA, output] = FISTA_REC(params); toc;
+
+error_FISTA = output.Resid_error; obj_FISTA = output.objective;
+fprintf('%s %.4f\n', 'Min RMSE for FISTA-PWLS reconstruction is:', min(error_FISTA(:)));
+
+Resid3D = (TomoPhantom - X_FISTA).^2;
+figure(2);
+subplot(1,2,1); imshow(X_FISTA(:,:,params.slice),[0 params.maxvalplot]); title('FISTA-LS reconstruction'); colorbar;
+subplot(1,2,2); imshow(Resid3D(:,:,params.slice),[0 0.1]); title('residual'); colorbar;
+figure(3);
+subplot(1,2,1); plot(error_FISTA); title('RMSE plot');
+subplot(1,2,2); plot(obj_FISTA); title('Objective plot');
+%%
+%%
+fprintf('%s\n', 'Reconstruction using OS-FISTA-GH with FGP-TV regularization...');
+clear params
+% define parameters
+params.proj_geom = proj_geom; % pass geometry to the function
+params.vol_geom = vol_geom;
+params.sino = single(sino3D_log); % sinogram
+params.iterFISTA = 15; %max number of outer iterations
+params.X_ideal = TomoPhantom; % ideal phantom
+params.weights = dataRaw./max(dataRaw(:)); % statistical weights for PWLS
+params.subsets = 12; % the number of subsets
+params.Regul_Lambda_FGPTV = 100; % TV regularization parameter for FGP-TV
+params.Ring_LambdaR_L1 = 0.02; % Soft-Thresh L1 ring variable parameter
+params.Ring_Alpha = 21; % to boost ring removal procedure
+params.show = 1; % visualize reconstruction on each iteration
+params.slice = 128; params.maxvalplot = 1.3;
+tic; [X_FISTA_GH_TV, output] = FISTA_REC(params); toc;
+
+error_FISTA_GH_TV = output.Resid_error; obj_FISTA_GH_TV = output.objective;
+fprintf('%s %.4f\n', 'Min RMSE for FISTA-PWLS reconstruction is:', min(error_FISTA_GH_TV(:)));
+
+Resid3D = (TomoPhantom - X_FISTA_GH_TV).^2;
+figure(2);
+subplot(1,2,1); imshow(X_FISTA_GH_TV(:,:,params.slice),[0 params.maxvalplot]); title('FISTA-LS reconstruction'); colorbar;
+subplot(1,2,2); imshow(Resid3D(:,:,params.slice),[0 0.1]); title('residual'); colorbar;
+figure(3);
+subplot(1,2,1); plot(error_FISTA_GH_TV); title('RMSE plot');
+subplot(1,2,2); plot(obj_FISTA_GH_TV); title('Objective plot');
+%%
\ No newline at end of file diff --git a/Wrappers/Matlab/demos/Demo_RealData3D_Parallel.m b/Wrappers/Matlab/demos/Demo_RealData3D_Parallel.m new file mode 100644 index 0000000..f82e0b0 --- /dev/null +++ b/Wrappers/Matlab/demos/Demo_RealData3D_Parallel.m @@ -0,0 +1,186 @@ +% Demonstration of tomographic 3D reconstruction from X-ray synchrotron +% dataset (dendrites) using various data fidelities +% ! It is advisable not to run the whole script, it will take lots of time to reconstruct the whole 3D data using many algorithms ! +clear +close all +%% +% % adding paths +addpath('../data/'); +addpath('../main_func/'); addpath('../main_func/regularizers_CPU/'); addpath('../main_func/regularizers_GPU/NL_Regul/'); addpath('../main_func/regularizers_GPU/Diffus_HO/'); +addpath('../supp/'); + +load('DendrRawData.mat') % load raw data of 3D dendritic set +angles_rad = angles*(pi/180); % conversion to radians +det_size = size(data_raw3D,1); % detectors dim +angSize = size(data_raw3D, 2); % angles dim +slices_tot = size(data_raw3D, 3); % no of slices +recon_size = 950; % reconstruction size + +Sino3D = zeros(det_size, angSize, slices_tot, 'single'); % log-corrected sino +% normalizing the data +for jj = 1:slices_tot + sino = data_raw3D(:,:,jj); + for ii = 1:angSize + Sino3D(:,ii,jj) = log((flats_ar(:,jj)-darks_ar(:,jj))./(single(sino(:,ii)) - darks_ar(:,jj))); + end +end + +Sino3D = Sino3D.*1000; +Weights3D = single(data_raw3D); % weights for PW model +clear data_raw3D +%% +% set projection/reconstruction geometry here +proj_geom = astra_create_proj_geom('parallel', 1, det_size, angles_rad); +vol_geom = astra_create_vol_geom(recon_size,recon_size); +%% +fprintf('%s\n', 'Reconstruction using FBP...'); +FBP = iradon(Sino3D(:,:,10), angles,recon_size); +figure; imshow(FBP , [0, 3]); title ('FBP reconstruction'); + +%--------FISTA_REC modular reconstruction alogrithms--------- +%% +fprintf('%s\n', 'Reconstruction using FISTA-OS-PWLS without regularization...'); +clear params +params.proj_geom = proj_geom; % pass geometry to the function +params.vol_geom = vol_geom; +params.sino = Sino3D; +params.iterFISTA = 18; +params.weights = Weights3D; +params.subsets = 8; % the number of ordered subsets +params.show = 1; +params.maxvalplot = 2.5; params.slice = 1; + +tic; [X_fista, outputFISTA] = FISTA_REC(params); toc; +figure; imshow(X_fista(:,:,params.slice) , [0, 2.5]); title ('FISTA-OS-PWLS reconstruction'); +%% +fprintf('%s\n', 'Reconstruction using FISTA-OS-PWLS-TV...'); +clear params +params.proj_geom = proj_geom; % pass geometry to the function +params.vol_geom = vol_geom; +params.sino = Sino3D; +params.iterFISTA = 18; +params.Regul_Lambda_FGPTV = 5.0000e+6; % TV regularization parameter for FGP-TV +params.weights = Weights3D; +params.subsets = 8; % the number of ordered subsets +params.show = 1; +params.maxvalplot = 2.5; params.slice = 10; + +tic; [X_fista_TV, outputTV] = FISTA_REC(params); toc; +figure; imshow(X_fista_TV(:,:,params.slice) , [0, 2.5]); title ('FISTA-OS-PWLS-TV reconstruction'); +%% +fprintf('%s\n', 'Reconstruction using FISTA-OS-GH-TV...'); +clear params +params.proj_geom = proj_geom; % pass geometry to the function +params.vol_geom = vol_geom; +params.sino = Sino3D(:,:,10); +params.iterFISTA = 18; +params.Regul_Lambda_FGPTV = 5.0000e+6; % TV regularization parameter for FGP-TV +params.Ring_LambdaR_L1 = 0.002; % Soft-Thresh L1 ring variable parameter +params.Ring_Alpha = 21; % to boost ring removal procedure +params.weights = Weights3D(:,:,10); +params.subsets = 8; % the number of ordered subsets +params.show = 1; +params.maxvalplot = 2.5; params.slice = 1; + +tic; [X_fista_GH_TV, outputGHTV] = FISTA_REC(params); toc; +figure; imshow(X_fista_GH_TV(:,:,params.slice) , [0, 2.5]); title ('FISTA-OS-GH-TV reconstruction'); +%% +fprintf('%s\n', 'Reconstruction using FISTA-OS-GH-TV-LLT...'); +clear params +params.proj_geom = proj_geom; % pass geometry to the function +params.vol_geom = vol_geom; +params.sino = Sino3D; +params.iterFISTA = 12; +params.Regul_Lambda_FGPTV = 5.0000e+6; % TV regularization parameter for FGP-TV +params.Regul_LambdaLLT = 100; % regularization parameter for LLT problem +params.Regul_tauLLT = 0.0005; % time-step parameter for the explicit scheme +params.Ring_LambdaR_L1 = 0.002; % Soft-Thresh L1 ring variable parameter +params.Ring_Alpha = 21; % to boost ring removal procedure +params.weights = Weights3D; +params.subsets = 16; % the number of ordered subsets +params.show = 1; +params.maxvalplot = 2.5; params.slice = 2; + +tic; [X_fista_GH_TVLLT, outputGH_TVLLT] = FISTA_REC(params); toc; +figure; imshow(X_fista_GH_TVLLT(:,:,params.slice) , [0, 2.5]); title ('FISTA-OS-GH-TV-LLT reconstruction'); + +%% +fprintf('%s\n', 'Reconstruction using FISTA-OS-GH-HigherOrderDiffusion...'); +% !GPU version! +clear params +params.proj_geom = proj_geom; % pass geometry to the function +params.vol_geom = vol_geom; +params.sino = Sino3D(:,:,1:5); +params.iterFISTA = 25; +params.Regul_LambdaDiffHO = 2; % DiffHO regularization parameter +params.Regul_DiffHO_EdgePar = 0.05; % threshold parameter +params.Regul_Iterations = 150; +params.Ring_LambdaR_L1 = 0.002; % Soft-Thresh L1 ring variable parameter +params.Ring_Alpha = 21; % to boost ring removal procedure +params.weights = Weights3D(:,:,1:5); +params.subsets = 16; % the number of ordered subsets +params.show = 1; +params.maxvalplot = 2.5; params.slice = 1; + +tic; [X_fista_GH_HO, outputHO] = FISTA_REC(params); toc; +figure; imshow(X_fista_GH_HO(:,:,params.slice) , [0, 2.5]); title ('FISTA-OS-HigherOrderDiffusion reconstruction'); + +%% +fprintf('%s\n', 'Reconstruction using FISTA-PB...'); +% !GPU version! +clear params +params.proj_geom = proj_geom; % pass geometry to the function +params.vol_geom = vol_geom; +params.sino = Sino3D(:,:,1); +params.iterFISTA = 25; +params.Regul_LambdaPatchBased_GPU = 3; % PB regularization parameter +params.Regul_PB_h = 0.04; % threhsold parameter +params.Regul_PB_SearchW = 3; +params.Regul_PB_SimilW = 1; +params.Ring_LambdaR_L1 = 0.002; % Soft-Thresh L1 ring variable parameter +params.Ring_Alpha = 21; % to boost ring removal procedure +params.weights = Weights3D(:,:,1); +params.show = 1; +params.maxvalplot = 2.5; params.slice = 1; + +tic; [X_fista_GH_PB, outputPB] = FISTA_REC(params); toc; +figure; imshow(X_fista_GH_PB(:,:,params.slice) , [0, 2.5]); title ('FISTA-OS-PB reconstruction'); +%% +fprintf('%s\n', 'Reconstruction using FISTA-OS-GH-TGV...'); +% still testing... +clear params +params.proj_geom = proj_geom; % pass geometry to the function +params.vol_geom = vol_geom; +params.sino = Sino3D; +params.iterFISTA = 12; +params.Regul_LambdaTGV = 0.5; % TGV regularization parameter +params.Regul_Iterations = 5; +params.Ring_LambdaR_L1 = 0.002; % Soft-Thresh L1 ring variable parameter +params.Ring_Alpha = 21; % to boost ring removal procedure +params.weights = Weights3D; +params.subsets = 16; % the number of ordered subsets +params.show = 1; +params.maxvalplot = 2.5; params.slice = 1; + +tic; [X_fista_GH_TGV, outputTGV] = FISTA_REC(params); toc; +figure; imshow(X_fista_GH_TGV(:,:,params.slice) , [0, 2.5]); title ('FISTA-OS-GH-TGV reconstruction'); + + +%% +% fprintf('%s\n', 'Reconstruction using FISTA-Student-TV...'); +% clear params +% params.proj_geom = proj_geom; % pass geometry to the function +% params.vol_geom = vol_geom; +% params.sino = Sino3D(:,:,10); +% params.iterFISTA = 50; +% params.L_const = 0.01; % Lipshitz constant +% params.Regul_LambdaTV = 0.008; % TV regularization parameter for FISTA-TV +% params.fidelity = 'student'; % choosing Student t penalty +% params.weights = Weights3D(:,:,10); +% params.show = 0; +% params.initialize = 1; +% params.maxvalplot = 2.5; params.slice = 1; +% +% tic; [X_fistaStudentTV] = FISTA_REC(params); toc; +% figure; imshow(X_fistaStudentTV(:,:,1), [0, 2.5]); title ('FISTA-Student-TV reconstruction'); +%% diff --git a/Wrappers/Matlab/demos/exportDemoRD2Data.m b/Wrappers/Matlab/demos/exportDemoRD2Data.m new file mode 100644 index 0000000..028353b --- /dev/null +++ b/Wrappers/Matlab/demos/exportDemoRD2Data.m @@ -0,0 +1,35 @@ +clear all +close all +%% +% % adding paths +addpath('../data/'); +addpath('../main_func/'); addpath('../main_func/regularizers_CPU/'); +addpath('../supp/'); + +load('DendrRawData.mat') % load raw data of 3D dendritic set +angles_rad = angles*(pi/180); % conversion to radians +size_det = size(data_raw3D,1); % detectors dim +angSize = size(data_raw3D, 2); % angles dim +slices_tot = size(data_raw3D, 3); % no of slices +recon_size = 950; % reconstruction size + +Sino3D = zeros(size_det, angSize, slices_tot, 'single'); % log-corrected sino +% normalizing the data +for jj = 1:slices_tot + sino = data_raw3D(:,:,jj); + for ii = 1:angSize + Sino3D(:,ii,jj) = log((flats_ar(:,jj)-darks_ar(:,jj))./(single(sino(:,ii)) - darks_ar(:,jj))); + end +end + +Sino3D = Sino3D.*1000; +Weights3D = single(data_raw3D); % weights for PW model +clear data_raw3D + +hdf5write('DendrData.h5', '/Weights3D', Weights3D) +hdf5write('DendrData.h5', '/Sino3D', Sino3D, 'WriteMode', 'append') +hdf5write('DendrData.h5', '/angles_rad', angles_rad, 'WriteMode', 'append') +hdf5write('DendrData.h5', '/size_det', size_det, 'WriteMode', 'append') +hdf5write('DendrData.h5', '/angSize', angSize, 'WriteMode', 'append') +hdf5write('DendrData.h5', '/slices_tot', slices_tot, 'WriteMode', 'append') +hdf5write('DendrData.h5', '/recon_size', recon_size, 'WriteMode', 'append')
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/compile_mex.m b/Wrappers/Matlab/mex_compile/compile_mex.m new file mode 100644 index 0000000..1353859 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/compile_mex.m @@ -0,0 +1,11 @@ +% compile mex's in Matlab once +cd regularizers_CPU/ + +mex LLT_model.c LLT_model_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +mex FGP_TV.c FGP_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +mex SplitBregman_TV.c SplitBregman_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +mex TGV_PD.c TGV_PD_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +mex PatchBased_Regul.c PatchBased_Regul_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" + +cd ../../ +cd demos diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV.c new file mode 100644 index 0000000..30cea1a --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV.c @@ -0,0 +1,216 @@ +/* +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 "FGP_TV_core.h" + +/* C-OMP implementation of FGP-TV [1] denoising/regularization model (2D/3D case) + * + * Input Parameters: + * 1. Noisy image/volume [REQUIRED] + * 2. lambda - regularization parameter [REQUIRED] + * 3. Number of iterations [OPTIONAL parameter] + * 4. eplsilon: tolerance constant [OPTIONAL parameter] + * 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter] + * + * Output: + * [1] Filtered/regularized image + * [2] last function value + * + * Example of image denoising: + * figure; + * Im = double(imread('lena_gray_256.tif'))/255; % loading image + * u0 = Im + .05*randn(size(Im)); % adding noise + * u = FGP_TV(single(u0), 0.05, 100, 1e-04); + * + * to compile with OMP support: mex FGP_TV.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" + * This function is based on the Matlab's code and paper by + * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems" + * + * D. Kazantsev, 2016-17 + * + */ + + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int number_of_dims, iter, dimX, dimY, dimZ, ll, j, count, methTV; + const int *dim_array; + float *A, *D=NULL, *D_old=NULL, *P1=NULL, *P2=NULL, *P3=NULL, *P1_old=NULL, *P2_old=NULL, *P3_old=NULL, *R1=NULL, *R2=NULL, *R3=NULL, lambda, tk, tkp1, re, re1, re_old, epsil; + + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + dim_array = mxGetDimensions(prhs[0]); + + /*Handling Matlab input data*/ + if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')"); + + A = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */ + lambda = (float) mxGetScalar(prhs[1]); /* regularization parameter */ + iter = 50; /* default iterations number */ + epsil = 0.0001; /* default tolerance constant */ + methTV = 0; /* default isotropic TV penalty */ + + if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5)) iter = (int) mxGetScalar(prhs[2]); /* iterations number */ + if ((nrhs == 4) || (nrhs == 5)) epsil = (float) mxGetScalar(prhs[3]); /* tolerance constant */ + if (nrhs == 5) { + char *penalty_type; + penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */ + if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',"); + if (strcmp(penalty_type, "l1") == 0) methTV = 1; /* enable 'l1' penalty */ + mxFree(penalty_type); + } + /*output function value (last iteration) */ + plhs[1] = mxCreateNumericMatrix(1, 1, mxSINGLE_CLASS, mxREAL); + float *funcvalA = (float *) mxGetData(plhs[1]); + + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } + + /*Handling Matlab output data*/ + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + + tk = 1.0f; + tkp1=1.0f; + count = 0; + re_old = 0.0f; + + if (number_of_dims == 2) { + dimZ = 1; /*2D case*/ + D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + D_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + P1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + P2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + R1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + R2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + + /* begin iterations */ + for(ll=0; ll<iter; ll++) { + + /* computing the gradient of the objective function */ + Obj_func2D(A, D, R1, R2, lambda, dimX, dimY); + + /*Taking a step towards minus of the gradient*/ + Grad_func2D(P1, P2, D, R1, R2, lambda, dimX, dimY); + + /* projection step */ + Proj_func2D(P1, P2, methTV, dimX, dimY); + + /*updating R and t*/ + tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f; + Rupd_func2D(P1, P1_old, P2, P2_old, R1, R2, tkp1, tk, dimX, dimY); + + /* calculate norm */ + re = 0.0f; re1 = 0.0f; + for(j=0; j<dimX*dimY*dimZ; j++) + { + re += pow(D[j] - D_old[j],2); + re1 += pow(D[j],2); + } + re = sqrt(re)/sqrt(re1); + if (re < epsil) count++; + if (count > 4) { + Obj_func_CALC2D(A, D, funcvalA, lambda, dimX, dimY); + break; } + + /* check that the residual norm is decreasing */ + if (ll > 2) { + if (re > re_old) { + Obj_func_CALC2D(A, D, funcvalA, lambda, dimX, dimY); + break; }} + re_old = re; + /*printf("%f %i %i \n", re, ll, count); */ + + /*storing old values*/ + copyIm(D, D_old, dimX, dimY, dimZ); + copyIm(P1, P1_old, dimX, dimY, dimZ); + copyIm(P2, P2_old, dimX, dimY, dimZ); + tk = tkp1; + + /* calculating the objective function value */ + if (ll == (iter-1)) Obj_func_CALC2D(A, D, funcvalA, lambda, dimX, dimY); + } + printf("FGP-TV iterations stopped at iteration %i with the function value %f \n", ll, funcvalA[0]); + } + if (number_of_dims == 3) { + D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + D_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + P1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + P2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + P3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + P1_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + P2_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + P3_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + R1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + R2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + R3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + + /* begin iterations */ + for(ll=0; ll<iter; ll++) { + + /* computing the gradient of the objective function */ + Obj_func3D(A, D, R1, R2, R3,lambda, dimX, dimY, dimZ); + + /*Taking a step towards minus of the gradient*/ + Grad_func3D(P1, P2, P3, D, R1, R2, R3, lambda, dimX, dimY, dimZ); + + /* projection step */ + Proj_func3D(P1, P2, P3, dimX, dimY, dimZ); + + /*updating R and t*/ + tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f; + Rupd_func3D(P1, P1_old, P2, P2_old, P3, P3_old, R1, R2, R3, tkp1, tk, dimX, dimY, dimZ); + + /* calculate norm - stopping rules*/ + re = 0.0f; re1 = 0.0f; + for(j=0; j<dimX*dimY*dimZ; j++) + { + re += pow(D[j] - D_old[j],2); + re1 += pow(D[j],2); + } + re = sqrt(re)/sqrt(re1); + /* stop if the norm residual is less than the tolerance EPS */ + if (re < epsil) count++; + if (count > 3) { + Obj_func_CALC3D(A, D, funcvalA, lambda, dimX, dimY, dimZ); + break;} + + /* check that the residual norm is decreasing */ + if (ll > 2) { + if (re > re_old) { + Obj_func_CALC3D(A, D, funcvalA, lambda, dimX, dimY, dimZ); + }} + re_old = re; + /*printf("%f %i %i \n", re, ll, count); */ + + /*storing old values*/ + copyIm(D, D_old, dimX, dimY, dimZ); + copyIm(P1, P1_old, dimX, dimY, dimZ); + copyIm(P2, P2_old, dimX, dimY, dimZ); + copyIm(P3, P3_old, dimX, dimY, dimZ); + tk = tkp1; + + if (ll == (iter-1)) Obj_func_CALC3D(A, D, funcvalA, lambda, dimX, dimY, dimZ); + } + printf("FGP-TV iterations stopped at iteration %i with the function value %f \n", ll, funcvalA[0]); + } +} diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV_core.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV_core.c new file mode 100644 index 0000000..03cd445 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV_core.c @@ -0,0 +1,266 @@ +/* +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 "FGP_TV_core.h" + +/* C-OMP implementation of FGP-TV [1] denoising/regularization model (2D/3D case) + * + * Input Parameters: + * 1. Noisy image/volume [REQUIRED] + * 2. lambda - regularization parameter [REQUIRED] + * 3. Number of iterations [OPTIONAL parameter] + * 4. eplsilon: tolerance constant [OPTIONAL parameter] + * 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter] + * + * Output: + * [1] Filtered/regularized image + * [2] last function value + * + * Example of image denoising: + * figure; + * Im = double(imread('lena_gray_256.tif'))/255; % loading image + * u0 = Im + .05*randn(size(Im)); % adding noise + * u = FGP_TV(single(u0), 0.05, 100, 1e-04); + * + * This function is based on the Matlab's code and paper by + * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems" + * + * D. Kazantsev, 2016-17 + * + */ + +/* 2D-case related Functions */ +/*****************************************************************/ +float Obj_func_CALC2D(float *A, float *D, float *funcvalA, float lambda, int dimX, int dimY) +{ + int i,j; + float f1, f2, val1, val2; + + /*data-related term */ + f1 = 0.0f; + for(i=0; i<dimX*dimY; i++) f1 += pow(D[i] - A[i],2); + + /*TV-related term */ + f2 = 0.0f; + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + /* boundary conditions */ + if (i == dimX-1) {val1 = 0.0f;} else {val1 = A[(i+1)*dimY + (j)] - A[(i)*dimY + (j)];} + if (j == dimY-1) {val2 = 0.0f;} else {val2 = A[(i)*dimY + (j+1)] - A[(i)*dimY + (j)];} + f2 += sqrt(pow(val1,2) + pow(val2,2)); + }} + + /* sum of two terms */ + funcvalA[0] = 0.5f*f1 + lambda*f2; + return *funcvalA; +} + +float Obj_func2D(float *A, float *D, float *R1, float *R2, float lambda, int dimX, int dimY) +{ + float val1, val2; + int i, j; +#pragma omp parallel for shared(A,D,R1,R2) private(i,j,val1,val2) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + /* boundary conditions */ + if (i == 0) { val1 = 0.0f; } + else { val1 = R1[(i - 1)*dimY + (j)]; } + if (j == 0) { val2 = 0.0f; } + else { val2 = R2[(i)*dimY + (j - 1)]; } + D[(i)*dimY + (j)] = A[(i)*dimY + (j)] - lambda*(R1[(i)*dimY + (j)] + R2[(i)*dimY + (j)] - val1 - val2); + } + } + return *D; +} +float Grad_func2D(float *P1, float *P2, float *D, float *R1, float *R2, float lambda, int dimX, int dimY) +{ + float val1, val2, multip; + int i, j; + multip = (1.0f / (8.0f*lambda)); +#pragma omp parallel for shared(P1,P2,D,R1,R2,multip) private(i,j,val1,val2) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + /* boundary conditions */ + if (i == dimX - 1) val1 = 0.0f; else val1 = D[(i)*dimY + (j)] - D[(i + 1)*dimY + (j)]; + if (j == dimY - 1) val2 = 0.0f; else val2 = D[(i)*dimY + (j)] - D[(i)*dimY + (j + 1)]; + P1[(i)*dimY + (j)] = R1[(i)*dimY + (j)] + multip*val1; + P2[(i)*dimY + (j)] = R2[(i)*dimY + (j)] + multip*val2; + } + } + return 1; +} +float Proj_func2D(float *P1, float *P2, int methTV, int dimX, int dimY) +{ + float val1, val2, denom; + int i, j; + if (methTV == 0) { + /* isotropic TV*/ +#pragma omp parallel for shared(P1,P2) private(i,j,denom) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + denom = pow(P1[(i)*dimY + (j)], 2) + pow(P2[(i)*dimY + (j)], 2); + if (denom > 1) { + P1[(i)*dimY + (j)] = P1[(i)*dimY + (j)] / sqrt(denom); + P2[(i)*dimY + (j)] = P2[(i)*dimY + (j)] / sqrt(denom); + } + } + } + } + else { + /* anisotropic TV*/ +#pragma omp parallel for shared(P1,P2) private(i,j,val1,val2) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + val1 = fabs(P1[(i)*dimY + (j)]); + val2 = fabs(P2[(i)*dimY + (j)]); + if (val1 < 1.0f) { val1 = 1.0f; } + if (val2 < 1.0f) { val2 = 1.0f; } + P1[(i)*dimY + (j)] = P1[(i)*dimY + (j)] / val1; + P2[(i)*dimY + (j)] = P2[(i)*dimY + (j)] / val2; + } + } + } + return 1; +} +float Rupd_func2D(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, float *R2, float tkp1, float tk, int dimX, int dimY) +{ + int i, j; + float multip; + multip = ((tk - 1.0f) / tkp1); +#pragma omp parallel for shared(P1,P2,P1_old,P2_old,R1,R2,multip) private(i,j) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + R1[(i)*dimY + (j)] = P1[(i)*dimY + (j)] + multip*(P1[(i)*dimY + (j)] - P1_old[(i)*dimY + (j)]); + R2[(i)*dimY + (j)] = P2[(i)*dimY + (j)] + multip*(P2[(i)*dimY + (j)] - P2_old[(i)*dimY + (j)]); + } + } + return 1; +} + +/* 3D-case related Functions */ +/*****************************************************************/ +float Obj_func_CALC3D(float *A, float *D, float *funcvalA, float lambda, int dimX, int dimY, int dimZ) +{ + int i,j,k; + float f1, f2, val1, val2, val3; + + /*data-related term */ + f1 = 0.0f; + for(i=0; i<dimX*dimY*dimZ; i++) f1 += pow(D[i] - A[i],2); + + /*TV-related term */ + f2 = 0.0f; + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + for(k=0; k<dimZ; k++) { + /* boundary conditions */ + if (i == dimX-1) {val1 = 0.0f;} else {val1 = A[(dimX*dimY)*k + (i+1)*dimY + (j)] - A[(dimX*dimY)*k + (i)*dimY + (j)];} + if (j == dimY-1) {val2 = 0.0f;} else {val2 = A[(dimX*dimY)*k + (i)*dimY + (j+1)] - A[(dimX*dimY)*k + (i)*dimY + (j)];} + if (k == dimZ-1) {val3 = 0.0f;} else {val3 = A[(dimX*dimY)*(k+1) + (i)*dimY + (j)] - A[(dimX*dimY)*k + (i)*dimY + (j)];} + f2 += sqrt(pow(val1,2) + pow(val2,2) + pow(val3,2)); + }}} + /* sum of two terms */ + funcvalA[0] = 0.5f*f1 + lambda*f2; + return *funcvalA; +} + +float Obj_func3D(float *A, float *D, float *R1, float *R2, float *R3, float lambda, int dimX, int dimY, int dimZ) +{ + float val1, val2, val3; + int i, j, k; +#pragma omp parallel for shared(A,D,R1,R2,R3) private(i,j,k,val1,val2,val3) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + for (k = 0; k<dimZ; k++) { + /* boundary conditions */ + if (i == 0) { val1 = 0.0f; } + else { val1 = R1[(dimX*dimY)*k + (i - 1)*dimY + (j)]; } + if (j == 0) { val2 = 0.0f; } + else { val2 = R2[(dimX*dimY)*k + (i)*dimY + (j - 1)]; } + if (k == 0) { val3 = 0.0f; } + else { val3 = R3[(dimX*dimY)*(k - 1) + (i)*dimY + (j)]; } + D[(dimX*dimY)*k + (i)*dimY + (j)] = A[(dimX*dimY)*k + (i)*dimY + (j)] - lambda*(R1[(dimX*dimY)*k + (i)*dimY + (j)] + R2[(dimX*dimY)*k + (i)*dimY + (j)] + R3[(dimX*dimY)*k + (i)*dimY + (j)] - val1 - val2 - val3); + } + } + } + return *D; +} +float Grad_func3D(float *P1, float *P2, float *P3, float *D, float *R1, float *R2, float *R3, float lambda, int dimX, int dimY, int dimZ) +{ + float val1, val2, val3, multip; + int i, j, k; + multip = (1.0f / (8.0f*lambda)); +#pragma omp parallel for shared(P1,P2,P3,D,R1,R2,R3,multip) private(i,j,k,val1,val2,val3) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + for (k = 0; k<dimZ; k++) { + /* boundary conditions */ + if (i == dimX - 1) val1 = 0.0f; else val1 = D[(dimX*dimY)*k + (i)*dimY + (j)] - D[(dimX*dimY)*k + (i + 1)*dimY + (j)]; + if (j == dimY - 1) val2 = 0.0f; else val2 = D[(dimX*dimY)*k + (i)*dimY + (j)] - D[(dimX*dimY)*k + (i)*dimY + (j + 1)]; + if (k == dimZ - 1) val3 = 0.0f; else val3 = D[(dimX*dimY)*k + (i)*dimY + (j)] - D[(dimX*dimY)*(k + 1) + (i)*dimY + (j)]; + P1[(dimX*dimY)*k + (i)*dimY + (j)] = R1[(dimX*dimY)*k + (i)*dimY + (j)] + multip*val1; + P2[(dimX*dimY)*k + (i)*dimY + (j)] = R2[(dimX*dimY)*k + (i)*dimY + (j)] + multip*val2; + P3[(dimX*dimY)*k + (i)*dimY + (j)] = R3[(dimX*dimY)*k + (i)*dimY + (j)] + multip*val3; + } + } + } + return 1; +} +float Proj_func3D(float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ) +{ + float val1, val2, val3; + int i, j, k; +#pragma omp parallel for shared(P1,P2,P3) private(i,j,k,val1,val2,val3) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + for (k = 0; k<dimZ; k++) { + val1 = fabs(P1[(dimX*dimY)*k + (i)*dimY + (j)]); + val2 = fabs(P2[(dimX*dimY)*k + (i)*dimY + (j)]); + val3 = fabs(P3[(dimX*dimY)*k + (i)*dimY + (j)]); + if (val1 < 1.0f) { val1 = 1.0f; } + if (val2 < 1.0f) { val2 = 1.0f; } + if (val3 < 1.0f) { val3 = 1.0f; } + + P1[(dimX*dimY)*k + (i)*dimY + (j)] = P1[(dimX*dimY)*k + (i)*dimY + (j)] / val1; + P2[(dimX*dimY)*k + (i)*dimY + (j)] = P2[(dimX*dimY)*k + (i)*dimY + (j)] / val2; + P3[(dimX*dimY)*k + (i)*dimY + (j)] = P3[(dimX*dimY)*k + (i)*dimY + (j)] / val3; + } + } + } + return 1; +} +float Rupd_func3D(float *P1, float *P1_old, float *P2, float *P2_old, float *P3, float *P3_old, float *R1, float *R2, float *R3, float tkp1, float tk, int dimX, int dimY, int dimZ) +{ + int i, j, k; + float multip; + multip = ((tk - 1.0f) / tkp1); +#pragma omp parallel for shared(P1,P2,P3,P1_old,P2_old,P3_old,R1,R2,R3,multip) private(i,j,k) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + for (k = 0; k<dimZ; k++) { + R1[(dimX*dimY)*k + (i)*dimY + (j)] = P1[(dimX*dimY)*k + (i)*dimY + (j)] + multip*(P1[(dimX*dimY)*k + (i)*dimY + (j)] - P1_old[(dimX*dimY)*k + (i)*dimY + (j)]); + R2[(dimX*dimY)*k + (i)*dimY + (j)] = P2[(dimX*dimY)*k + (i)*dimY + (j)] + multip*(P2[(dimX*dimY)*k + (i)*dimY + (j)] - P2_old[(dimX*dimY)*k + (i)*dimY + (j)]); + R3[(dimX*dimY)*k + (i)*dimY + (j)] = P3[(dimX*dimY)*k + (i)*dimY + (j)] + multip*(P3[(dimX*dimY)*k + (i)*dimY + (j)] - P3_old[(dimX*dimY)*k + (i)*dimY + (j)]); + } + } + } + return 1; +} + + diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV_core.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV_core.h new file mode 100644 index 0000000..6430bf2 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV_core.h @@ -0,0 +1,71 @@ +/* +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 <math.h> +#include <stdlib.h> +#include <memory.h> +#include <stdio.h> +#include "omp.h" +#include "utils.h" + +/* C-OMP implementation of FGP-TV [1] denoising/regularization model (2D/3D case) +* +* Input Parameters: +* 1. Noisy image/volume [REQUIRED] +* 2. lambda - regularization parameter [REQUIRED] +* 3. Number of iterations [OPTIONAL parameter] +* 4. eplsilon: tolerance constant [OPTIONAL parameter] +* 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter] +* +* Output: +* [1] Filtered/regularized image +* [2] last function value +* +* Example of image denoising: +* figure; +* Im = double(imread('lena_gray_256.tif'))/255; % loading image +* u0 = Im + .05*randn(size(Im)); % adding noise +* u = FGP_TV(single(u0), 0.05, 100, 1e-04); +* +* to compile with OMP support: mex FGP_TV.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +* This function is based on the Matlab's code and paper by +* [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems" +* +* D. Kazantsev, 2016-17 +* +*/ +#ifdef __cplusplus +extern "C" { +#endif +//float copyIm(float *A, float *B, int dimX, int dimY, int dimZ); +float Obj_func2D(float *A, float *D, float *R1, float *R2, float lambda, int dimX, int dimY); +float Grad_func2D(float *P1, float *P2, float *D, float *R1, float *R2, float lambda, int dimX, int dimY); +float Proj_func2D(float *P1, float *P2, int methTV, int dimX, int dimY); +float Rupd_func2D(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, float *R2, float tkp1, float tk, int dimX, int dimY); +float Obj_func_CALC2D(float *A, float *D, float *funcvalA, float lambda, int dimX, int dimY); + +float Obj_func3D(float *A, float *D, float *R1, float *R2, float *R3, float lambda, int dimX, int dimY, int dimZ); +float Grad_func3D(float *P1, float *P2, float *P3, float *D, float *R1, float *R2, float *R3, float lambda, int dimX, int dimY, int dimZ); +float Proj_func3D(float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ); +float Rupd_func3D(float *P1, float *P1_old, float *P2, float *P2_old, float *P3, float *P3_old, float *R1, float *R2, float *R3, float tkp1, float tk, int dimX, int dimY, int dimZ); +float Obj_func_CALC3D(float *A, float *D, float *funcvalA, float lambda, int dimX, int dimY, int dimZ); +#ifdef __cplusplus +} +#endif
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model.c new file mode 100644 index 0000000..0b07b47 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model.c @@ -0,0 +1,169 @@ +/* +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 "mex.h" +#include "matrix.h" +#include "LLT_model_core.h" + +/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model of higher order regularization penalty +* +* Input Parameters: +* 1. U0 - original noise image/volume +* 2. lambda - regularization parameter +* 3. tau - time-step for explicit scheme +* 4. iter - iterations number +* 5. epsil - tolerance constant (to terminate earlier) +* 6. switcher - default is 0, switch to (1) to restrictive smoothing in Z dimension (in test) +* +* Output: +* Filtered/regularized image +* +* Example: +* figure; +* Im = double(imread('lena_gray_256.tif'))/255; % loading image +* u0 = Im + .03*randn(size(Im)); % adding noise +* [Den] = LLT_model(single(u0), 10, 0.1, 1); +* +* +* to compile with OMP support: mex LLT_model.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +* References: Lysaker, Lundervold and Tai (LLT) 2003, IEEE +* +* 28.11.16/Harwell +*/ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int number_of_dims, iter, dimX, dimY, dimZ, ll, j, count, switcher; + const int *dim_array; + float *U0, *U=NULL, *U_old=NULL, *D1=NULL, *D2=NULL, *D3=NULL, lambda, tau, re, re1, epsil, re_old; + unsigned short *Map=NULL; + + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + dim_array = mxGetDimensions(prhs[0]); + + /*Handling Matlab input data*/ + U0 = (float *) mxGetData(prhs[0]); /*origanal noise image/volume*/ + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input in single precision is required"); } + lambda = (float) mxGetScalar(prhs[1]); /*regularization parameter*/ + tau = (float) mxGetScalar(prhs[2]); /* time-step */ + iter = (int) mxGetScalar(prhs[3]); /*iterations number*/ + epsil = (float) mxGetScalar(prhs[4]); /* tolerance constant */ + switcher = (int) mxGetScalar(prhs[5]); /*switch on (1) restrictive smoothing in Z dimension*/ + + /*Handling Matlab output data*/ + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = 1; + + if (number_of_dims == 2) { + /*2D case*/ + U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + D1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + D2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + } + else if (number_of_dims == 3) { + /*3D case*/ + dimZ = dim_array[2]; + U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + U_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + D1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + D2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + D3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + if (switcher != 0) { + Map = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array, mxUINT16_CLASS, mxREAL)); + } + } + else {mexErrMsgTxt("The input data should be 2D or 3D");} + + /*Copy U0 to U*/ + copyIm(U0, U, dimX, dimY, dimZ); + + count = 1; + re_old = 0.0f; + if (number_of_dims == 2) { + for(ll = 0; ll < iter; ll++) { + + copyIm(U, U_old, dimX, dimY, dimZ); + + /*estimate inner derrivatives */ + der2D(U, D1, D2, dimX, dimY, dimZ); + /* calculate div^2 and update */ + div_upd2D(U0, U, D1, D2, dimX, dimY, dimZ, lambda, tau); + + /* calculate norm to terminate earlier */ + re = 0.0f; re1 = 0.0f; + for(j=0; j<dimX*dimY*dimZ; j++) + { + re += pow(U_old[j] - U[j],2); + re1 += pow(U_old[j],2); + } + re = sqrt(re)/sqrt(re1); + if (re < epsil) count++; + if (count > 4) break; + + /* check that the residual norm is decreasing */ + if (ll > 2) { + if (re > re_old) break; + } + re_old = re; + + } /*end of iterations*/ + printf("HO iterations stopped at iteration: %i\n", ll); + } + /*3D version*/ + if (number_of_dims == 3) { + + if (switcher == 1) { + /* apply restrictive smoothing */ + calcMap(U, Map, dimX, dimY, dimZ); + /*clear outliers */ + cleanMap(Map, dimX, dimY, dimZ); + } + for(ll = 0; ll < iter; ll++) { + + copyIm(U, U_old, dimX, dimY, dimZ); + + /*estimate inner derrivatives */ + der3D(U, D1, D2, D3, dimX, dimY, dimZ); + /* calculate div^2 and update */ + div_upd3D(U0, U, D1, D2, D3, Map, switcher, dimX, dimY, dimZ, lambda, tau); + + /* calculate norm to terminate earlier */ + re = 0.0f; re1 = 0.0f; + for(j=0; j<dimX*dimY*dimZ; j++) + { + re += pow(U_old[j] - U[j],2); + re1 += pow(U_old[j],2); + } + re = sqrt(re)/sqrt(re1); + if (re < epsil) count++; + if (count > 4) break; + + /* check that the residual norm is decreasing */ + if (ll > 2) { + if (re > re_old) break; + } + re_old = re; + + } /*end of iterations*/ + printf("HO iterations stopped at iteration: %i\n", ll); + } +} diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model_core.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model_core.c new file mode 100644 index 0000000..3a853d2 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model_core.c @@ -0,0 +1,318 @@ +/* +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 "LLT_model_core.h" + +/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model of higher order regularization penalty +* +* Input Parameters: +* 1. U0 - origanal noise image/volume +* 2. lambda - regularization parameter +* 3. tau - time-step for explicit scheme +* 4. iter - iterations number +* 5. epsil - tolerance constant (to terminate earlier) +* 6. switcher - default is 0, switch to (1) to restrictive smoothing in Z dimension (in test) +* +* Output: +* Filtered/regularized image +* +* Example: +* figure; +* Im = double(imread('lena_gray_256.tif'))/255; % loading image +* u0 = Im + .03*randn(size(Im)); % adding noise +* [Den] = LLT_model(single(u0), 10, 0.1, 1); +* +* References: Lysaker, Lundervold and Tai (LLT) 2003, IEEE +* +* 28.11.16/Harwell +*/ + + +float der2D(float *U, float *D1, float *D2, int dimX, int dimY, int dimZ) +{ + int i, j, i_p, i_m, j_m, j_p; + float dxx, dyy, denom_xx, denom_yy; +#pragma omp parallel for shared(U,D1,D2) private(i, j, i_p, i_m, j_m, j_p, denom_xx, denom_yy, dxx, dyy) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + /* symmetric boundary conditions (Neuman) */ + i_p = i + 1; if (i_p == dimX) i_p = i - 1; + i_m = i - 1; if (i_m < 0) i_m = i + 1; + j_p = j + 1; if (j_p == dimY) j_p = j - 1; + j_m = j - 1; if (j_m < 0) j_m = j + 1; + + dxx = U[i_p*dimY + j] - 2.0f*U[i*dimY + j] + U[i_m*dimY + j]; + dyy = U[i*dimY + j_p] - 2.0f*U[i*dimY + j] + U[i*dimY + j_m]; + + denom_xx = fabs(dxx) + EPS; + denom_yy = fabs(dyy) + EPS; + + D1[i*dimY + j] = dxx / denom_xx; + D2[i*dimY + j] = dyy / denom_yy; + } + } + return 1; +} +float div_upd2D(float *U0, float *U, float *D1, float *D2, int dimX, int dimY, int dimZ, float lambda, float tau) +{ + int i, j, i_p, i_m, j_m, j_p; + float div, dxx, dyy; +#pragma omp parallel for shared(U,U0,D1,D2) private(i, j, i_p, i_m, j_m, j_p, div, dxx, dyy) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + /* symmetric boundary conditions (Neuman) */ + i_p = i + 1; if (i_p == dimX) i_p = i - 1; + i_m = i - 1; if (i_m < 0) i_m = i + 1; + j_p = j + 1; if (j_p == dimY) j_p = j - 1; + j_m = j - 1; if (j_m < 0) j_m = j + 1; + + dxx = D1[i_p*dimY + j] - 2.0f*D1[i*dimY + j] + D1[i_m*dimY + j]; + dyy = D2[i*dimY + j_p] - 2.0f*D2[i*dimY + j] + D2[i*dimY + j_m]; + + div = dxx + dyy; + + U[i*dimY + j] = U[i*dimY + j] - tau*div - tau*lambda*(U[i*dimY + j] - U0[i*dimY + j]); + } + } + return *U0; +} + +float der3D(float *U, float *D1, float *D2, float *D3, int dimX, int dimY, int dimZ) +{ + int i, j, k, i_p, i_m, j_m, j_p, k_p, k_m; + float dxx, dyy, dzz, denom_xx, denom_yy, denom_zz; +#pragma omp parallel for shared(U,D1,D2,D3) private(i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, denom_xx, denom_yy, denom_zz, dxx, dyy, dzz) + for (i = 0; i<dimX; i++) { + /* symmetric boundary conditions (Neuman) */ + i_p = i + 1; if (i_p == dimX) i_p = i - 1; + i_m = i - 1; if (i_m < 0) i_m = i + 1; + for (j = 0; j<dimY; j++) { + j_p = j + 1; if (j_p == dimY) j_p = j - 1; + j_m = j - 1; if (j_m < 0) j_m = j + 1; + for (k = 0; k<dimZ; k++) { + k_p = k + 1; if (k_p == dimZ) k_p = k - 1; + k_m = k - 1; if (k_m < 0) k_m = k + 1; + + dxx = U[dimX*dimY*k + i_p*dimY + j] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k + i_m*dimY + j]; + dyy = U[dimX*dimY*k + i*dimY + j_p] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k + i*dimY + j_m]; + dzz = U[dimX*dimY*k_p + i*dimY + j] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k_m + i*dimY + j]; + + denom_xx = fabs(dxx) + EPS; + denom_yy = fabs(dyy) + EPS; + denom_zz = fabs(dzz) + EPS; + + D1[dimX*dimY*k + i*dimY + j] = dxx / denom_xx; + D2[dimX*dimY*k + i*dimY + j] = dyy / denom_yy; + D3[dimX*dimY*k + i*dimY + j] = dzz / denom_zz; + + } + } + } + return 1; +} + +float div_upd3D(float *U0, float *U, float *D1, float *D2, float *D3, unsigned short *Map, int switcher, int dimX, int dimY, int dimZ, float lambda, float tau) +{ + int i, j, k, i_p, i_m, j_m, j_p, k_p, k_m; + float div, dxx, dyy, dzz; +#pragma omp parallel for shared(U,U0,D1,D2,D3) private(i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, div, dxx, dyy, dzz) + for (i = 0; i<dimX; i++) { + /* symmetric boundary conditions (Neuman) */ + i_p = i + 1; if (i_p == dimX) i_p = i - 1; + i_m = i - 1; if (i_m < 0) i_m = i + 1; + for (j = 0; j<dimY; j++) { + j_p = j + 1; if (j_p == dimY) j_p = j - 1; + j_m = j - 1; if (j_m < 0) j_m = j + 1; + for (k = 0; k<dimZ; k++) { + k_p = k + 1; if (k_p == dimZ) k_p = k - 1; + k_m = k - 1; if (k_m < 0) k_m = k + 1; + // k_p1 = k + 2; if (k_p1 >= dimZ) k_p1 = k - 2; + // k_m1 = k - 2; if (k_m1 < 0) k_m1 = k + 2; + + dxx = D1[dimX*dimY*k + i_p*dimY + j] - 2.0f*D1[dimX*dimY*k + i*dimY + j] + D1[dimX*dimY*k + i_m*dimY + j]; + dyy = D2[dimX*dimY*k + i*dimY + j_p] - 2.0f*D2[dimX*dimY*k + i*dimY + j] + D2[dimX*dimY*k + i*dimY + j_m]; + dzz = D3[dimX*dimY*k_p + i*dimY + j] - 2.0f*D3[dimX*dimY*k + i*dimY + j] + D3[dimX*dimY*k_m + i*dimY + j]; + + if ((switcher == 1) && (Map[dimX*dimY*k + i*dimY + j] == 0)) dzz = 0; + div = dxx + dyy + dzz; + + // if (switcher == 1) { + // if (Map2[dimX*dimY*k + i*dimY + j] == 0) dzz2 = 0; + //else dzz2 = D4[dimX*dimY*k_p1 + i*dimY + j] - 2.0f*D4[dimX*dimY*k + i*dimY + j] + D4[dimX*dimY*k_m1 + i*dimY + j]; + // div = dzz + dzz2; + // } + + // dzz = D3[dimX*dimY*k_p + i*dimY + j] - 2.0f*D3[dimX*dimY*k + i*dimY + j] + D3[dimX*dimY*k_m + i*dimY + j]; + // dzz2 = D4[dimX*dimY*k_p1 + i*dimY + j] - 2.0f*D4[dimX*dimY*k + i*dimY + j] + D4[dimX*dimY*k_m1 + i*dimY + j]; + // div = dzz + dzz2; + + U[dimX*dimY*k + i*dimY + j] = U[dimX*dimY*k + i*dimY + j] - tau*div - tau*lambda*(U[dimX*dimY*k + i*dimY + j] - U0[dimX*dimY*k + i*dimY + j]); + } + } + } + return *U0; +} + +// float der3D_2(float *U, float *D1, float *D2, float *D3, float *D4, int dimX, int dimY, int dimZ) +// { +// int i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, k_p1, k_m1; +// float dxx, dyy, dzz, dzz2, denom_xx, denom_yy, denom_zz, denom_zz2; +// #pragma omp parallel for shared(U,D1,D2,D3,D4) private(i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, denom_xx, denom_yy, denom_zz, denom_zz2, dxx, dyy, dzz, dzz2, k_p1, k_m1) +// for(i=0; i<dimX; i++) { +// /* symmetric boundary conditions (Neuman) */ +// i_p = i + 1; if (i_p == dimX) i_p = i - 1; +// i_m = i - 1; if (i_m < 0) i_m = i + 1; +// for(j=0; j<dimY; j++) { +// j_p = j + 1; if (j_p == dimY) j_p = j - 1; +// j_m = j - 1; if (j_m < 0) j_m = j + 1; +// for(k=0; k<dimZ; k++) { +// k_p = k + 1; if (k_p == dimZ) k_p = k - 1; +// k_m = k - 1; if (k_m < 0) k_m = k + 1; +// k_p1 = k + 2; if (k_p1 >= dimZ) k_p1 = k - 2; +// k_m1 = k - 2; if (k_m1 < 0) k_m1 = k + 2; +// +// dxx = U[dimX*dimY*k + i_p*dimY + j] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k + i_m*dimY + j]; +// dyy = U[dimX*dimY*k + i*dimY + j_p] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k + i*dimY + j_m]; +// dzz = U[dimX*dimY*k_p + i*dimY + j] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k_m + i*dimY + j]; +// dzz2 = U[dimX*dimY*k_p1 + i*dimY + j] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k_m1 + i*dimY + j]; +// +// denom_xx = fabs(dxx) + EPS; +// denom_yy = fabs(dyy) + EPS; +// denom_zz = fabs(dzz) + EPS; +// denom_zz2 = fabs(dzz2) + EPS; +// +// D1[dimX*dimY*k + i*dimY + j] = dxx/denom_xx; +// D2[dimX*dimY*k + i*dimY + j] = dyy/denom_yy; +// D3[dimX*dimY*k + i*dimY + j] = dzz/denom_zz; +// D4[dimX*dimY*k + i*dimY + j] = dzz2/denom_zz2; +// }}} +// return 1; +// } + +float calcMap(float *U, unsigned short *Map, int dimX, int dimY, int dimZ) +{ + int i, j, k, i1, j1, i2, j2, windowSize; + float val1, val2, thresh_val, maxval; + windowSize = 1; + thresh_val = 0.0001; /*thresh_val = 0.0035;*/ + + /* normalize volume first */ + maxval = 0.0f; + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + for (k = 0; k<dimZ; k++) { + if (U[dimX*dimY*k + i*dimY + j] > maxval) maxval = U[dimX*dimY*k + i*dimY + j]; + } + } + } + + if (maxval != 0.0f) { + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + for (k = 0; k<dimZ; k++) { + U[dimX*dimY*k + i*dimY + j] = U[dimX*dimY*k + i*dimY + j] / maxval; + } + } + } + } + else { + printf("%s \n", "Maximum value is zero!"); + return 0; + } + +#pragma omp parallel for shared(U,Map) private(i, j, k, i1, j1, i2, j2, val1, val2) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + for (k = 0; k<dimZ; k++) { + + Map[dimX*dimY*k + i*dimY + j] = 0; + // Map2[dimX*dimY*k + i*dimY + j] = 0; + + val1 = 0.0f; val2 = 0.0f; + for (i1 = -windowSize; i1 <= windowSize; i1++) { + for (j1 = -windowSize; j1 <= windowSize; j1++) { + i2 = i + i1; + j2 = j + j1; + + if ((i2 >= 0) && (i2 < dimX) && (j2 >= 0) && (j2 < dimY)) { + if (k == 0) { + val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k + 1) + i2*dimY + j2], 2); + // val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+2) + i2*dimY + j2],2); + } + else if (k == dimZ - 1) { + val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k - 1) + i2*dimY + j2], 2); + // val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-2) + i2*dimY + j2],2); + } + // else if (k == 1) { + // val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-1) + i2*dimY + j2],2); + // val2 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+1) + i2*dimY + j2],2); + // val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+2) + i2*dimY + j2],2); + // } + // else if (k == dimZ-2) { + // val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-1) + i2*dimY + j2],2); + // val2 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+1) + i2*dimY + j2],2); + // val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-2) + i2*dimY + j2],2); + // } + else { + val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k - 1) + i2*dimY + j2], 2); + val2 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k + 1) + i2*dimY + j2], 2); + // val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-2) + i2*dimY + j2],2); + // val4 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+2) + i2*dimY + j2],2); + } + } + } + } + + val1 = 0.111f*val1; val2 = 0.111f*val2; + // val3 = 0.111f*val3; val4 = 0.111f*val4; + if ((val1 <= thresh_val) && (val2 <= thresh_val)) Map[dimX*dimY*k + i*dimY + j] = 1; + // if ((val3 <= thresh_val) && (val4 <= thresh_val)) Map2[dimX*dimY*k + i*dimY + j] = 1; + } + } + } + return 1; +} + +float cleanMap(unsigned short *Map, int dimX, int dimY, int dimZ) +{ + int i, j, k, i1, j1, i2, j2, counter; +#pragma omp parallel for shared(Map) private(i, j, k, i1, j1, i2, j2, counter) + for (i = 0; i<dimX; i++) { + for (j = 0; j<dimY; j++) { + for (k = 0; k<dimZ; k++) { + + counter = 0; + for (i1 = -3; i1 <= 3; i1++) { + for (j1 = -3; j1 <= 3; j1++) { + i2 = i + i1; + j2 = j + j1; + if ((i2 >= 0) && (i2 < dimX) && (j2 >= 0) && (j2 < dimY)) { + if (Map[dimX*dimY*k + i2*dimY + j2] == 0) counter++; + } + } + } + if (counter < 24) Map[dimX*dimY*k + i*dimY + j] = 1; + } + } + } + return *Map; +} + + +/*********************3D *********************/
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model_core.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model_core.h new file mode 100644 index 0000000..13fce5a --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model_core.h @@ -0,0 +1,46 @@ +/* +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 <math.h> +#include <stdlib.h> +#include <memory.h> +#include <stdio.h> +#include "omp.h" +#include "utils.h" + +#define EPS 0.01 + +/* 2D functions */ +#ifdef __cplusplus +extern "C" { +#endif +float der2D(float *U, float *D1, float *D2, int dimX, int dimY, int dimZ); +float div_upd2D(float *U0, float *U, float *D1, float *D2, int dimX, int dimY, int dimZ, float lambda, float tau); + +float der3D(float *U, float *D1, float *D2, float *D3, int dimX, int dimY, int dimZ); +float div_upd3D(float *U0, float *U, float *D1, float *D2, float *D3, unsigned short *Map, int switcher, int dimX, int dimY, int dimZ, float lambda, float tau); + +float calcMap(float *U, unsigned short *Map, int dimX, int dimY, int dimZ); +float cleanMap(unsigned short *Map, int dimX, int dimY, int dimZ); + +//float copyIm(float *A, float *U, int dimX, int dimY, int dimZ); +#ifdef __cplusplus +} +#endif
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul.c new file mode 100644 index 0000000..9c925df --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul.c @@ -0,0 +1,140 @@ +/*
+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 "mex.h"
+#include "matrix.h"
+#include "PatchBased_Regul_core.h"
+
+
+/* C-OMP implementation of patch-based (PB) regularization (2D and 3D cases).
+ * This method finds self-similar patches in data and performs one fixed point iteration to mimimize the PB penalty function
+ *
+ * References: 1. Yang Z. & Jacob M. "Nonlocal Regularization of Inverse Problems"
+ * 2. Kazantsev D. et al. "4D-CT reconstruction with unified spatial-temporal patch-based regularization"
+ *
+ * Input Parameters:
+ * 1. Image (2D or 3D) [required]
+ * 2. ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window) [optional]
+ * 3. ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window) [optional]
+ * 4. h - parameter for the PB penalty function [optional]
+ * 5. lambda - regularization parameter [optional]
+
+ * Output:
+ * 1. regularized (denoised) Image (N x N)/volume (N x N x N)
+ *
+ * 2D denoising example in Matlab:
+ Im = double(imread('lena_gray_256.tif'))/255; % loading image
+ u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+ ImDen = PatchBased_Regul(single(u0), 3, 1, 0.08, 0.05);
+ *
+ * Matlab + C/mex compilers needed
+ * to compile with OMP support: mex PatchBased_Regul.c CFLAGS="\$CFLAGS -fopenmp -Wall" LDFLAGS="\$LDFLAGS -fopenmp"
+ *
+ * D. Kazantsev *
+ * 02/07/2014
+ * Harwell, UK
+ */
+
+
+void mexFunction(
+ int nlhs, mxArray *plhs[],
+ int nrhs, const mxArray *prhs[])
+{
+ int N, M, Z, numdims, SearchW, SimilW, SearchW_real, padXY, newsizeX, newsizeY, newsizeZ, switchpad_crop;
+ const int *dims;
+ float *A, *B=NULL, *Ap=NULL, *Bp=NULL, h, lambda;
+
+ numdims = mxGetNumberOfDimensions(prhs[0]);
+ dims = mxGetDimensions(prhs[0]);
+
+ N = dims[0];
+ M = dims[1];
+ Z = dims[2];
+
+ if ((numdims < 2) || (numdims > 3)) {mexErrMsgTxt("The input is 2D image or 3D volume");}
+ if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input in single precision is required"); }
+
+ if(nrhs != 5) mexErrMsgTxt("Five inputs reqired: Image(2D,3D), SearchW, SimilW, Threshold, Regularization parameter");
+
+ /*Handling inputs*/
+ A = (float *) mxGetData(prhs[0]); /* the image/volume to regularize/filter */
+ SearchW_real = 3; /*default value*/
+ SimilW = 1; /*default value*/
+ h = 0.1;
+ lambda = 0.1;
+
+ if ((nrhs == 2) || (nrhs == 3) || (nrhs == 4) || (nrhs == 5)) SearchW_real = (int) mxGetScalar(prhs[1]); /* the searching window ratio */
+ if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5)) SimilW = (int) mxGetScalar(prhs[2]); /* the similarity window ratio */
+ if ((nrhs == 4) || (nrhs == 5)) h = (float) mxGetScalar(prhs[3]); /* parameter for the PB filtering function */
+ if ((nrhs == 5)) lambda = (float) mxGetScalar(prhs[4]); /* regularization parameter */
+
+
+ if (h <= 0) mexErrMsgTxt("Parmeter for the PB penalty function should be > 0");
+ if (lambda <= 0) mexErrMsgTxt(" Regularization parmeter should be > 0");
+
+ SearchW = SearchW_real + 2*SimilW;
+
+ /* SearchW_full = 2*SearchW + 1; */ /* the full searching window size */
+ /* SimilW_full = 2*SimilW + 1; */ /* the full similarity window size */
+
+ padXY = SearchW + 2*SimilW; /* padding sizes */
+ newsizeX = N + 2*(padXY); /* the X size of the padded array */
+ newsizeY = M + 2*(padXY); /* the Y size of the padded array */
+ newsizeZ = Z + 2*(padXY); /* the Z size of the padded array */
+ int N_dims[] = {newsizeX, newsizeY, newsizeZ};
+
+ /******************************2D case ****************************/
+ if (numdims == 2) {
+ /*Handling output*/
+ B = (float*)mxGetData(plhs[0] = mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
+ /*allocating memory for the padded arrays */
+ Ap = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
+ Bp = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
+ /**************************************************************************/
+ /*Perform padding of image A to the size of [newsizeX * newsizeY] */
+ switchpad_crop = 0; /*padding*/
+ pad_crop(A, Ap, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
+
+ /* Do PB regularization with the padded array */
+ PB_FUNC2D(Ap, Bp, newsizeY, newsizeX, padXY, SearchW, SimilW, (float)h, (float)lambda);
+
+ switchpad_crop = 1; /*cropping*/
+ pad_crop(Bp, B, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
+ }
+ else
+ {
+ /******************************3D case ****************************/
+ /*Handling output*/
+ B = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dims, mxSINGLE_CLASS, mxREAL));
+ /*allocating memory for the padded arrays */
+ Ap = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
+ Bp = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
+ /**************************************************************************/
+
+ /*Perform padding of image A to the size of [newsizeX * newsizeY * newsizeZ] */
+ switchpad_crop = 0; /*padding*/
+ pad_crop(A, Ap, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
+
+ /* Do PB regularization with the padded array */
+ PB_FUNC3D(Ap, Bp, newsizeY, newsizeX, newsizeZ, padXY, SearchW, SimilW, (float)h, (float)lambda);
+
+ switchpad_crop = 1; /*cropping*/
+ pad_crop(Bp, B, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
+ } /*end else ndims*/
+}
diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul_core.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul_core.c new file mode 100644 index 0000000..acfb464 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul_core.c @@ -0,0 +1,213 @@ +/* +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 Kazanteev +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 "PatchBased_Regul_core.h" + +/* C-OMP implementation of patch-based (PB) regularization (2D and 3D cases). + * This method finds self-similar patches in data and performs one fixed point iteration to mimimize the PB penalty function + * + * References: 1. Yang Z. & Jacob M. "Nonlocal Regularization of Inverse Problems" + * 2. Kazantsev D. et al. "4D-CT reconstruction with unified spatial-temporal patch-based regularization" + * + * Input Parameters: + * 1. Image (2D or 3D) [required] + * 2. ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window) [optional] + * 3. ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window) [optional] + * 4. h - parameter for the PB penalty function [optional] + * 5. lambda - regularization parameter [optional] + + * Output: + * 1. regularized (denoised) Image (N x N)/volume (N x N x N) + * + * 2D denoising example in Matlab: + Im = double(imread('lena_gray_256.tif'))/255; % loading image + u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise + ImDen = PatchBased_Regul(single(u0), 3, 1, 0.08, 0.05); + + * D. Kazantsev * + * 02/07/2014 + * Harwell, UK + */ + +/*2D version function */ +float PB_FUNC2D(float *A, float *B, int dimX, int dimY, int padXY, int SearchW, int SimilW, float h, float lambda) +{ + int i, j, i_n, j_n, i_m, j_m, i_p, j_p, i_l, j_l, i1, j1, i2, j2, i3, j3, i5,j5, count, SimilW_full; + float *Eucl_Vec, h2, denh2, normsum, Weight, Weight_norm, value, denom, WeightGlob, t1; + + /*SearchW_full = 2*SearchW + 1; */ /* the full searching window size */ + SimilW_full = 2*SimilW + 1; /* the full similarity window size */ + h2 = h*h; + denh2 = 1/(2*h2); + + /*Gaussian kernel */ + Eucl_Vec = (float*) calloc (SimilW_full*SimilW_full,sizeof(float)); + count = 0; + for(i_n=-SimilW; i_n<=SimilW; i_n++) { + for(j_n=-SimilW; j_n<=SimilW; j_n++) { + t1 = pow(((float)i_n), 2) + pow(((float)j_n), 2); + Eucl_Vec[count] = exp(-(t1)/(2*SimilW*SimilW)); + count = count + 1; + }} /*main neighb loop */ + + /*The NLM code starts here*/ + /* setting OMP here */ + #pragma omp parallel for shared (A, B, dimX, dimY, Eucl_Vec, lambda, denh2) private(denom, i, j, WeightGlob, count, i1, j1, i2, j2, i3, j3, i5, j5, Weight_norm, normsum, i_m, j_m, i_n, j_n, i_l, j_l, i_p, j_p, Weight, value) + + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + if (((i >= padXY) && (i < dimX-padXY)) && ((j >= padXY) && (j < dimY-padXY))) { + + /* Massive Search window loop */ + Weight_norm = 0; value = 0.0; + for(i_m=-SearchW; i_m<=SearchW; i_m++) { + for(j_m=-SearchW; j_m<=SearchW; j_m++) { + /*checking boundaries*/ + i1 = i+i_m; j1 = j+j_m; + + WeightGlob = 0.0; + /* if inside the searching window */ + for(i_l=-SimilW; i_l<=SimilW; i_l++) { + for(j_l=-SimilW; j_l<=SimilW; j_l++) { + i2 = i1+i_l; j2 = j1+j_l; + + i3 = i+i_l; j3 = j+j_l; /*coordinates of the inner patch loop */ + + count = 0; normsum = 0.0; + for(i_p=-SimilW; i_p<=SimilW; i_p++) { + for(j_p=-SimilW; j_p<=SimilW; j_p++) { + i5 = i2 + i_p; j5 = j2 + j_p; + normsum = normsum + Eucl_Vec[count]*pow(A[(i3+i_p)*dimY+(j3+j_p)]-A[i5*dimY+j5], 2); + count = count + 1; + }} + if (normsum != 0) Weight = (exp(-normsum*denh2)); + else Weight = 0.0; + WeightGlob += Weight; + }} + + value += A[i1*dimY+j1]*WeightGlob; + Weight_norm += WeightGlob; + }} /*search window loop end*/ + + /* the final loop to average all values in searching window with weights */ + denom = 1 + lambda*Weight_norm; + B[i*dimY+j] = (A[i*dimY+j] + lambda*value)/denom; + } + }} /*main loop*/ + return (*B); + free(Eucl_Vec); +} + +/*3D version*/ + float PB_FUNC3D(float *A, float *B, int dimX, int dimY, int dimZ, int padXY, int SearchW, int SimilW, float h, float lambda) + { + int SimilW_full, count, i, j, k, i_n, j_n, k_n, i_m, j_m, k_m, i_p, j_p, k_p, i_l, j_l, k_l, i1, j1, k1, i2, j2, k2, i3, j3, k3, i5, j5, k5; + float *Eucl_Vec, h2, denh2, normsum, Weight, Weight_norm, value, denom, WeightGlob; + + /*SearchW_full = 2*SearchW + 1; */ /* the full searching window size */ + SimilW_full = 2*SimilW + 1; /* the full similarity window size */ + h2 = h*h; + denh2 = 1/(2*h2); + + /*Gaussian kernel */ + Eucl_Vec = (float*) calloc (SimilW_full*SimilW_full*SimilW_full,sizeof(float)); + count = 0; + for(i_n=-SimilW; i_n<=SimilW; i_n++) { + for(j_n=-SimilW; j_n<=SimilW; j_n++) { + for(k_n=-SimilW; k_n<=SimilW; k_n++) { + Eucl_Vec[count] = exp(-(pow((float)i_n, 2) + pow((float)j_n, 2) + pow((float)k_n, 2))/(2*SimilW*SimilW*SimilW)); + count = count + 1; + }}} /*main neighb loop */ + + /*The NLM code starts here*/ + /* setting OMP here */ + #pragma omp parallel for shared (A, B, dimX, dimY, dimZ, Eucl_Vec, lambda, denh2) private(denom, i, j, k, WeightGlob,count, i1, j1, k1, i2, j2, k2, i3, j3, k3, i5, j5, k5, Weight_norm, normsum, i_m, j_m, k_m, i_n, j_n, k_n, i_l, j_l, k_l, i_p, j_p, k_p, Weight, value) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + for(k=0; k<dimZ; k++) { + if (((i >= padXY) && (i < dimX-padXY)) && ((j >= padXY) && (j < dimY-padXY)) && ((k >= padXY) && (k < dimZ-padXY))) { + /* take all elements around the pixel of interest */ + /* Massive Search window loop */ + Weight_norm = 0; value = 0.0; + for(i_m=-SearchW; i_m<=SearchW; i_m++) { + for(j_m=-SearchW; j_m<=SearchW; j_m++) { + for(k_m=-SearchW; k_m<=SearchW; k_m++) { + /*checking boundaries*/ + i1 = i+i_m; j1 = j+j_m; k1 = k+k_m; + + WeightGlob = 0.0; + /* if inside the searching window */ + for(i_l=-SimilW; i_l<=SimilW; i_l++) { + for(j_l=-SimilW; j_l<=SimilW; j_l++) { + for(k_l=-SimilW; k_l<=SimilW; k_l++) { + i2 = i1+i_l; j2 = j1+j_l; k2 = k1+k_l; + + i3 = i+i_l; j3 = j+j_l; k3 = k+k_l; /*coordinates of the inner patch loop */ + + count = 0; normsum = 0.0; + for(i_p=-SimilW; i_p<=SimilW; i_p++) { + for(j_p=-SimilW; j_p<=SimilW; j_p++) { + for(k_p=-SimilW; k_p<=SimilW; k_p++) { + i5 = i2 + i_p; j5 = j2 + j_p; k5 = k2 + k_p; + normsum = normsum + Eucl_Vec[count]*pow(A[(dimX*dimY)*(k3+k_p)+(i3+i_p)*dimY+(j3+j_p)]-A[(dimX*dimY)*k5 + i5*dimY+j5], 2); + count = count + 1; + }}} + if (normsum != 0) Weight = (exp(-normsum*denh2)); + else Weight = 0.0; + WeightGlob += Weight; + }}} + value += A[(dimX*dimY)*k1 + i1*dimY+j1]*WeightGlob; + Weight_norm += WeightGlob; + + }}} /*search window loop end*/ + + /* the final loop to average all values in searching window with weights */ + denom = 1 + lambda*Weight_norm; + B[(dimX*dimY)*k + i*dimY+j] = (A[(dimX*dimY)*k + i*dimY+j] + lambda*value)/denom; + } + }}} /*main loop*/ + free(Eucl_Vec); + return *B; +} + +float pad_crop(float *A, float *Ap, int OldSizeX, int OldSizeY, int OldSizeZ, int NewSizeX, int NewSizeY, int NewSizeZ, int padXY, int switchpad_crop) +{ + /* padding-cropping function */ + int i,j,k; + if (NewSizeZ > 1) { + for (i=0; i < NewSizeX; i++) { + for (j=0; j < NewSizeY; j++) { + for (k=0; k < NewSizeZ; k++) { + if (((i >= padXY) && (i < NewSizeX-padXY)) && ((j >= padXY) && (j < NewSizeY-padXY)) && ((k >= padXY) && (k < NewSizeZ-padXY))) { + if (switchpad_crop == 0) Ap[NewSizeX*NewSizeY*k + i*NewSizeY+j] = A[OldSizeX*OldSizeY*(k - padXY) + (i-padXY)*(OldSizeY)+(j-padXY)]; + else Ap[OldSizeX*OldSizeY*(k - padXY) + (i-padXY)*(OldSizeY)+(j-padXY)] = A[NewSizeX*NewSizeY*k + i*NewSizeY+j]; + } + }}} + } + else { + for (i=0; i < NewSizeX; i++) { + for (j=0; j < NewSizeY; j++) { + if (((i >= padXY) && (i < NewSizeX-padXY)) && ((j >= padXY) && (j < NewSizeY-padXY))) { + if (switchpad_crop == 0) Ap[i*NewSizeY+j] = A[(i-padXY)*(OldSizeY)+(j-padXY)]; + else Ap[(i-padXY)*(OldSizeY)+(j-padXY)] = A[i*NewSizeY+j]; + } + }} + } + return *Ap; +}
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul_core.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul_core.h new file mode 100644 index 0000000..d4a8a46 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul_core.h @@ -0,0 +1,69 @@ +/* +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 Kazanteev +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. +*/ + +#define _USE_MATH_DEFINES + +//#include <matrix.h> +#include <math.h> +#include <stdlib.h> +#include <memory.h> +#include <stdio.h> +#include "omp.h" + +/* C-OMP implementation of patch-based (PB) regularization (2D and 3D cases). +* This method finds self-similar patches in data and performs one fixed point iteration to mimimize the PB penalty function +* +* References: 1. Yang Z. & Jacob M. "Nonlocal Regularization of Inverse Problems" +* 2. Kazantsev D. et al. "4D-CT reconstruction with unified spatial-temporal patch-based regularization" +* +* Input Parameters (mandatory): +* 1. Image (2D or 3D) +* 2. ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window) +* 3. ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window) +* 4. h - parameter for the PB penalty function +* 5. lambda - regularization parameter + +* Output: +* 1. regularized (denoised) Image (N x N)/volume (N x N x N) +* +* Quick 2D denoising example in Matlab: +Im = double(imread('lena_gray_256.tif'))/255; % loading image +u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise +ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); +* +* Please see more tests in a file: +TestTemporalSmoothing.m + +* +* Matlab + C/mex compilers needed +* to compile with OMP support: mex PB_Regul_CPU.c CFLAGS="\$CFLAGS -fopenmp -Wall" LDFLAGS="\$LDFLAGS -fopenmp" +* +* D. Kazantsev * +* 02/07/2014 +* Harwell, UK +*/ +#ifdef __cplusplus +extern "C" { +#endif +float pad_crop(float *A, float *Ap, int OldSizeX, int OldSizeY, int OldSizeZ, int NewSizeX, int NewSizeY, int NewSizeZ, int padXY, int switchpad_crop); +float PB_FUNC2D(float *A, float *B, int dimX, int dimY, int padXY, int SearchW, int SimilW, float h, float lambda); +float PB_FUNC3D(float *A, float *B, int dimX, int dimY, int dimZ, int padXY, int SearchW, int SimilW, float h, float lambda); +#ifdef __cplusplus +} +#endif
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV.c new file mode 100644 index 0000000..38f6a9d --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV.c @@ -0,0 +1,179 @@ +/* +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 "mex.h" +#include <matrix.h> +#include "SplitBregman_TV_core.h" + +/* C-OMP implementation of Split Bregman - TV denoising-regularization model (2D/3D) + * + * Input Parameters: + * 1. Noisy image/volume + * 2. lambda - regularization parameter + * 3. Number of iterations [OPTIONAL parameter] + * 4. eplsilon - tolerance constant [OPTIONAL parameter] + * 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter] + * + * Output: + * Filtered/regularized image + * + * Example: + * figure; + * Im = double(imread('lena_gray_256.tif'))/255; % loading image + * u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; + * u = SplitBregman_TV(single(u0), 10, 30, 1e-04); + * + * to compile with OMP support: mex SplitBregman_TV.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" + * References: + * The Split Bregman Method for L1 Regularized Problems, by Tom Goldstein and Stanley Osher. + * D. Kazantsev, 2016* + */ + + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int number_of_dims, iter, dimX, dimY, dimZ, ll, j, count, methTV; + const int *dim_array; + float *A, *U=NULL, *U_old=NULL, *Dx=NULL, *Dy=NULL, *Dz=NULL, *Bx=NULL, *By=NULL, *Bz=NULL, lambda, mu, epsil, re, re1, re_old; + + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + dim_array = mxGetDimensions(prhs[0]); + + /*Handling Matlab input data*/ + if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')"); + + /*Handling Matlab input data*/ + A = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */ + mu = (float) mxGetScalar(prhs[1]); /* regularization parameter */ + iter = 35; /* default iterations number */ + epsil = 0.0001; /* default tolerance constant */ + methTV = 0; /* default isotropic TV penalty */ + if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5)) iter = (int) mxGetScalar(prhs[2]); /* iterations number */ + if ((nrhs == 4) || (nrhs == 5)) epsil = (float) mxGetScalar(prhs[3]); /* tolerance constant */ + if (nrhs == 5) { + char *penalty_type; + penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */ + if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',"); + if (strcmp(penalty_type, "l1") == 0) methTV = 1; /* enable 'l1' penalty */ + mxFree(penalty_type); + } + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } + + lambda = 2.0f*mu; + count = 1; + re_old = 0.0f; + /*Handling Matlab output data*/ + dimY = dim_array[0]; dimX = dim_array[1]; dimZ = dim_array[2]; + + if (number_of_dims == 2) { + dimZ = 1; /*2D case*/ + U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + Dx = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + Dy = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + Bx = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + By = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + + copyIm(A, U, dimX, dimY, dimZ); /*initialize */ + + /* begin outer SB iterations */ + for(ll=0; ll<iter; ll++) { + + /*storing old values*/ + copyIm(U, U_old, dimX, dimY, dimZ); + + /*GS iteration */ + gauss_seidel2D(U, A, Dx, Dy, Bx, By, dimX, dimY, lambda, mu); + + if (methTV == 1) updDxDy_shrinkAniso2D(U, Dx, Dy, Bx, By, dimX, dimY, lambda); + else updDxDy_shrinkIso2D(U, Dx, Dy, Bx, By, dimX, dimY, lambda); + + updBxBy2D(U, Dx, Dy, Bx, By, dimX, dimY); + + /* calculate norm to terminate earlier */ + re = 0.0f; re1 = 0.0f; + for(j=0; j<dimX*dimY*dimZ; j++) + { + re += pow(U_old[j] - U[j],2); + re1 += pow(U_old[j],2); + } + re = sqrt(re)/sqrt(re1); + if (re < epsil) count++; + if (count > 4) break; + + /* check that the residual norm is decreasing */ + if (ll > 2) { + if (re > re_old) break; + } + re_old = re; + /*printf("%f %i %i \n", re, ll, count); */ + + /*copyIm(U_old, U, dimX, dimY, dimZ); */ + } + printf("SB iterations stopped at iteration: %i\n", ll); + } + if (number_of_dims == 3) { + U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + U_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + Dx = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + Dy = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + Dz = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + Bx = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + By = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + Bz = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + + copyIm(A, U, dimX, dimY, dimZ); /*initialize */ + + /* begin outer SB iterations */ + for(ll=0; ll<iter; ll++) { + + /*storing old values*/ + copyIm(U, U_old, dimX, dimY, dimZ); + + /*GS iteration */ + gauss_seidel3D(U, A, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda, mu); + + if (methTV == 1) updDxDyDz_shrinkAniso3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda); + else updDxDyDz_shrinkIso3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda); + + updBxByBz3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ); + + /* calculate norm to terminate earlier */ + re = 0.0f; re1 = 0.0f; + for(j=0; j<dimX*dimY*dimZ; j++) + { + re += pow(U[j] - U_old[j],2); + re1 += pow(U[j],2); + } + re = sqrt(re)/sqrt(re1); + if (re < epsil) count++; + if (count > 4) break; + + /* check that the residual norm is decreasing */ + if (ll > 2) { + if (re > re_old) break; } + /*printf("%f %i %i \n", re, ll, count); */ + re_old = re; + } + printf("SB iterations stopped at iteration: %i\n", ll); + } +}
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV_core.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV_core.c new file mode 100644 index 0000000..4109a4b --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV_core.c @@ -0,0 +1,259 @@ +/* +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 "SplitBregman_TV_core.h" + +/* C-OMP implementation of Split Bregman - TV denoising-regularization model (2D/3D) +* +* Input Parameters: +* 1. Noisy image/volume +* 2. lambda - regularization parameter +* 3. Number of iterations [OPTIONAL parameter] +* 4. eplsilon - tolerance constant [OPTIONAL parameter] +* 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter] +* +* Output: +* Filtered/regularized image +* +* Example: +* figure; +* Im = double(imread('lena_gray_256.tif'))/255; % loading image +* u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; +* u = SplitBregman_TV(single(u0), 10, 30, 1e-04); +* +* References: +* The Split Bregman Method for L1 Regularized Problems, by Tom Goldstein and Stanley Osher. +* D. Kazantsev, 2016* +*/ + + +/* 2D-case related Functions */ +/*****************************************************************/ +float gauss_seidel2D(float *U, float *A, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda, float mu) +{ + float sum, normConst; + int i,j,i1,i2,j1,j2; + normConst = 1.0f/(mu + 4.0f*lambda); + +#pragma omp parallel for shared(U) private(i,j,i1,i2,j1,j2,sum) + for(i=0; i<dimX; i++) { + /* symmetric boundary conditions (Neuman) */ + i1 = i+1; if (i1 == dimX) i1 = i-1; + i2 = i-1; if (i2 < 0) i2 = i+1; + for(j=0; j<dimY; j++) { + /* symmetric boundary conditions (Neuman) */ + j1 = j+1; if (j1 == dimY) j1 = j-1; + j2 = j-1; if (j2 < 0) j2 = j+1; + + sum = Dx[(i2)*dimY + (j)] - Dx[(i)*dimY + (j)] + Dy[(i)*dimY + (j2)] - Dy[(i)*dimY + (j)] - Bx[(i2)*dimY + (j)] + Bx[(i)*dimY + (j)] - By[(i)*dimY + (j2)] + By[(i)*dimY + (j)]; + sum += (U[(i1)*dimY + (j)] + U[(i2)*dimY + (j)] + U[(i)*dimY + (j1)] + U[(i)*dimY + (j2)]); + sum *= lambda; + sum += mu*A[(i)*dimY + (j)]; + U[(i)*dimY + (j)] = normConst*sum; + }} + return *U; +} + +float updDxDy_shrinkAniso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda) +{ + int i,j,i1,j1; + float val1, val11, val2, val22, denom_lam; + denom_lam = 1.0f/lambda; +#pragma omp parallel for shared(U,denom_lam) private(i,j,i1,j1,val1,val11,val2,val22) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + /* symmetric boundary conditions (Neuman) */ + i1 = i+1; if (i1 == dimX) i1 = i-1; + j1 = j+1; if (j1 == dimY) j1 = j-1; + + val1 = (U[(i1)*dimY + (j)] - U[(i)*dimY + (j)]) + Bx[(i)*dimY + (j)]; + val2 = (U[(i)*dimY + (j1)] - U[(i)*dimY + (j)]) + By[(i)*dimY + (j)]; + + val11 = fabs(val1) - denom_lam; if (val11 < 0) val11 = 0; + val22 = fabs(val2) - denom_lam; if (val22 < 0) val22 = 0; + + if (val1 !=0) Dx[(i)*dimY + (j)] = (val1/fabs(val1))*val11; else Dx[(i)*dimY + (j)] = 0; + if (val2 !=0) Dy[(i)*dimY + (j)] = (val2/fabs(val2))*val22; else Dy[(i)*dimY + (j)] = 0; + + }} + return 1; +} +float updDxDy_shrinkIso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda) +{ + int i,j,i1,j1; + float val1, val11, val2, denom, denom_lam; + denom_lam = 1.0f/lambda; + +#pragma omp parallel for shared(U,denom_lam) private(i,j,i1,j1,val1,val11,val2,denom) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + /* symmetric boundary conditions (Neuman) */ + i1 = i+1; if (i1 == dimX) i1 = i-1; + j1 = j+1; if (j1 == dimY) j1 = j-1; + + val1 = (U[(i1)*dimY + (j)] - U[(i)*dimY + (j)]) + Bx[(i)*dimY + (j)]; + val2 = (U[(i)*dimY + (j1)] - U[(i)*dimY + (j)]) + By[(i)*dimY + (j)]; + + denom = sqrt(val1*val1 + val2*val2); + + val11 = (denom - denom_lam); if (val11 < 0) val11 = 0.0f; + + if (denom != 0.0f) { + Dx[(i)*dimY + (j)] = val11*(val1/denom); + Dy[(i)*dimY + (j)] = val11*(val2/denom); + } + else { + Dx[(i)*dimY + (j)] = 0; + Dy[(i)*dimY + (j)] = 0; + } + }} + return 1; +} +float updBxBy2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY) +{ + int i,j,i1,j1; +#pragma omp parallel for shared(U) private(i,j,i1,j1) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + /* symmetric boundary conditions (Neuman) */ + i1 = i+1; if (i1 == dimX) i1 = i-1; + j1 = j+1; if (j1 == dimY) j1 = j-1; + + Bx[(i)*dimY + (j)] = Bx[(i)*dimY + (j)] + ((U[(i1)*dimY + (j)] - U[(i)*dimY + (j)]) - Dx[(i)*dimY + (j)]); + By[(i)*dimY + (j)] = By[(i)*dimY + (j)] + ((U[(i)*dimY + (j1)] - U[(i)*dimY + (j)]) - Dy[(i)*dimY + (j)]); + }} + return 1; +} + + +/* 3D-case related Functions */ +/*****************************************************************/ +float gauss_seidel3D(float *U, float *A, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda, float mu) +{ + float normConst, d_val, b_val, sum; + int i,j,i1,i2,j1,j2,k,k1,k2; + normConst = 1.0f/(mu + 6.0f*lambda); +#pragma omp parallel for shared(U) private(i,j,i1,i2,j1,j2,k,k1,k2,d_val,b_val,sum) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + for(k=0; k<dimZ; k++) { + /* symmetric boundary conditions (Neuman) */ + i1 = i+1; if (i1 == dimX) i1 = i-1; + i2 = i-1; if (i2 < 0) i2 = i+1; + j1 = j+1; if (j1 == dimY) j1 = j-1; + j2 = j-1; if (j2 < 0) j2 = j+1; + k1 = k+1; if (k1 == dimZ) k1 = k-1; + k2 = k-1; if (k2 < 0) k2 = k+1; + + d_val = Dx[(dimX*dimY)*k + (i2)*dimY + (j)] - Dx[(dimX*dimY)*k + (i)*dimY + (j)] + Dy[(dimX*dimY)*k + (i)*dimY + (j2)] - Dy[(dimX*dimY)*k + (i)*dimY + (j)] + Dz[(dimX*dimY)*k2 + (i)*dimY + (j)] - Dz[(dimX*dimY)*k + (i)*dimY + (j)]; + b_val = -Bx[(dimX*dimY)*k + (i2)*dimY + (j)] + Bx[(dimX*dimY)*k + (i)*dimY + (j)] - By[(dimX*dimY)*k + (i)*dimY + (j2)] + By[(dimX*dimY)*k + (i)*dimY + (j)] - Bz[(dimX*dimY)*k2 + (i)*dimY + (j)] + Bz[(dimX*dimY)*k + (i)*dimY + (j)]; + sum = d_val + b_val; + sum += U[(dimX*dimY)*k + (i1)*dimY + (j)] + U[(dimX*dimY)*k + (i2)*dimY + (j)] + U[(dimX*dimY)*k + (i)*dimY + (j1)] + U[(dimX*dimY)*k + (i)*dimY + (j2)] + U[(dimX*dimY)*k1 + (i)*dimY + (j)] + U[(dimX*dimY)*k2 + (i)*dimY + (j)]; + sum *= lambda; + sum += mu*A[(dimX*dimY)*k + (i)*dimY + (j)]; + U[(dimX*dimY)*k + (i)*dimY + (j)] = normConst*sum; + }}} + return *U; +} + +float updDxDyDz_shrinkAniso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda) +{ + int i,j,i1,j1,k,k1,index; + float val1, val11, val2, val22, val3, val33, denom_lam; + denom_lam = 1.0f/lambda; +#pragma omp parallel for shared(U,denom_lam) private(index,i,j,i1,j1,k,k1,val1,val11,val2,val22,val3,val33) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + for(k=0; k<dimZ; k++) { + index = (dimX*dimY)*k + (i)*dimY + (j); + /* symmetric boundary conditions (Neuman) */ + i1 = i+1; if (i1 == dimX) i1 = i-1; + j1 = j+1; if (j1 == dimY) j1 = j-1; + k1 = k+1; if (k1 == dimZ) k1 = k-1; + + val1 = (U[(dimX*dimY)*k + (i1)*dimY + (j)] - U[index]) + Bx[index]; + val2 = (U[(dimX*dimY)*k + (i)*dimY + (j1)] - U[index]) + By[index]; + val3 = (U[(dimX*dimY)*k1 + (i)*dimY + (j)] - U[index]) + Bz[index]; + + val11 = fabs(val1) - denom_lam; if (val11 < 0) val11 = 0; + val22 = fabs(val2) - denom_lam; if (val22 < 0) val22 = 0; + val33 = fabs(val3) - denom_lam; if (val33 < 0) val33 = 0; + + if (val1 !=0) Dx[index] = (val1/fabs(val1))*val11; else Dx[index] = 0; + if (val2 !=0) Dy[index] = (val2/fabs(val2))*val22; else Dy[index] = 0; + if (val3 !=0) Dz[index] = (val3/fabs(val3))*val33; else Dz[index] = 0; + + }}} + return 1; +} +float updDxDyDz_shrinkIso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda) +{ + int i,j,i1,j1,k,k1,index; + float val1, val11, val2, val3, denom, denom_lam; + denom_lam = 1.0f/lambda; +#pragma omp parallel for shared(U,denom_lam) private(index,denom,i,j,i1,j1,k,k1,val1,val11,val2,val3) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + for(k=0; k<dimZ; k++) { + index = (dimX*dimY)*k + (i)*dimY + (j); + /* symmetric boundary conditions (Neuman) */ + i1 = i+1; if (i1 == dimX) i1 = i-1; + j1 = j+1; if (j1 == dimY) j1 = j-1; + k1 = k+1; if (k1 == dimZ) k1 = k-1; + + val1 = (U[(dimX*dimY)*k + (i1)*dimY + (j)] - U[index]) + Bx[index]; + val2 = (U[(dimX*dimY)*k + (i)*dimY + (j1)] - U[index]) + By[index]; + val3 = (U[(dimX*dimY)*k1 + (i)*dimY + (j)] - U[index]) + Bz[index]; + + denom = sqrt(val1*val1 + val2*val2 + val3*val3); + + val11 = (denom - denom_lam); if (val11 < 0) val11 = 0.0f; + + if (denom != 0.0f) { + Dx[index] = val11*(val1/denom); + Dy[index] = val11*(val2/denom); + Dz[index] = val11*(val3/denom); + } + else { + Dx[index] = 0; + Dy[index] = 0; + Dz[index] = 0; + } + }}} + return 1; +} +float updBxByBz3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ) +{ + int i,j,k,i1,j1,k1; +#pragma omp parallel for shared(U) private(i,j,k,i1,j1,k1) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + for(k=0; k<dimZ; k++) { + /* symmetric boundary conditions (Neuman) */ + i1 = i+1; if (i1 == dimX) i1 = i-1; + j1 = j+1; if (j1 == dimY) j1 = j-1; + k1 = k+1; if (k1 == dimZ) k1 = k-1; + + Bx[(dimX*dimY)*k + (i)*dimY + (j)] = Bx[(dimX*dimY)*k + (i)*dimY + (j)] + ((U[(dimX*dimY)*k + (i1)*dimY + (j)] - U[(dimX*dimY)*k + (i)*dimY + (j)]) - Dx[(dimX*dimY)*k + (i)*dimY + (j)]); + By[(dimX*dimY)*k + (i)*dimY + (j)] = By[(dimX*dimY)*k + (i)*dimY + (j)] + ((U[(dimX*dimY)*k + (i)*dimY + (j1)] - U[(dimX*dimY)*k + (i)*dimY + (j)]) - Dy[(dimX*dimY)*k + (i)*dimY + (j)]); + Bz[(dimX*dimY)*k + (i)*dimY + (j)] = Bz[(dimX*dimY)*k + (i)*dimY + (j)] + ((U[(dimX*dimY)*k1 + (i)*dimY + (j)] - U[(dimX*dimY)*k + (i)*dimY + (j)]) - Dz[(dimX*dimY)*k + (i)*dimY + (j)]); + + }}} + return 1; +} diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV_core.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV_core.h new file mode 100644 index 0000000..6ed3ff9 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV_core.h @@ -0,0 +1,69 @@ +/* +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 <math.h> +#include <stdlib.h> +#include <memory.h> +#include <stdio.h> +#include "omp.h" + +#include "utils.h" + +/* C-OMP implementation of Split Bregman - TV denoising-regularization model (2D/3D) +* +* Input Parameters: +* 1. Noisy image/volume +* 2. lambda - regularization parameter +* 3. Number of iterations [OPTIONAL parameter] +* 4. eplsilon - tolerance constant [OPTIONAL parameter] +* 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter] +* +* Output: +* Filtered/regularized image +* +* Example: +* figure; +* Im = double(imread('lena_gray_256.tif'))/255; % loading image +* u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; +* u = SplitBregman_TV(single(u0), 10, 30, 1e-04); +* +* to compile with OMP support: mex SplitBregman_TV.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +* References: +* The Split Bregman Method for L1 Regularized Problems, by Tom Goldstein and Stanley Osher. +* D. Kazantsev, 2016* +*/ + +#ifdef __cplusplus +extern "C" { +#endif + +//float copyIm(float *A, float *B, int dimX, int dimY, int dimZ); +float gauss_seidel2D(float *U, float *A, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda, float mu); +float updDxDy_shrinkAniso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda); +float updDxDy_shrinkIso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda); +float updBxBy2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY); + +float gauss_seidel3D(float *U, float *A, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda, float mu); +float updDxDyDz_shrinkAniso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda); +float updDxDyDz_shrinkIso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda); +float updBxByBz3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ); + +#ifdef __cplusplus +} +#endif
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD.c new file mode 100644 index 0000000..c9cb440 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD.c @@ -0,0 +1,144 @@ +/* +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 "TGV_PD_core.h" +#include "mex.h" + +/* C-OMP implementation of Primal-Dual denoising method for + * Total Generilized Variation (TGV)-L2 model (2D case only) + * + * Input Parameters: + * 1. Noisy image/volume (2D) + * 2. lambda - regularization parameter + * 3. parameter to control first-order term (alpha1) + * 4. parameter to control the second-order term (alpha0) + * 5. Number of CP iterations + * + * Output: + * Filtered/regularized image + * + * Example: + * figure; + * Im = double(imread('lena_gray_256.tif'))/255; % loading image + * u0 = Im + .03*randn(size(Im)); % adding noise + * tic; u = TGV_PD(single(u0), 0.02, 1.3, 1, 550); toc; + * + * to compile with OMP support: mex TGV_PD.c TGV_PD_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" + * References: + * K. Bredies "Total Generalized Variation" + * + * 28.11.16/Harwell + */ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int number_of_dims, iter, dimX, dimY, dimZ, ll; + const int *dim_array; + float *A, *U, *U_old, *P1, *P2, *Q1, *Q2, *Q3, *V1, *V1_old, *V2, *V2_old, lambda, L2, tau, sigma, alpha1, alpha0; + + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + dim_array = mxGetDimensions(prhs[0]); + + /*Handling Matlab input data*/ + A = (float *) mxGetData(prhs[0]); /*origanal noise image/volume*/ + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input in single precision is required"); } + lambda = (float) mxGetScalar(prhs[1]); /*regularization parameter*/ + alpha1 = (float) mxGetScalar(prhs[2]); /*first-order term*/ + alpha0 = (float) mxGetScalar(prhs[3]); /*second-order term*/ + iter = (int) mxGetScalar(prhs[4]); /*iterations number*/ + if(nrhs != 5) mexErrMsgTxt("Five input parameters is reqired: Image(2D/3D), Regularization parameter, alpha1, alpha0, Iterations"); + + /*Handling Matlab output data*/ + dimX = dim_array[0]; dimY = dim_array[1]; + + if (number_of_dims == 2) { + /*2D case*/ + dimZ = 1; + U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + + /*dual variables*/ + P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + + Q1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + Q2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + Q3 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + + U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + + V1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + V1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + V2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + V2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + + + /*printf("%i \n", i);*/ + L2 = 12.0f; /*Lipshitz constant*/ + tau = 1.0/pow(L2,0.5); + sigma = 1.0/pow(L2,0.5); + + /*Copy A to U*/ + copyIm(A, U, dimX, dimY, dimZ); + + /* Here primal-dual iterations begin for 2D */ + for(ll = 0; ll < iter; ll++) { + + /* Calculate Dual Variable P */ + DualP_2D(U, V1, V2, P1, P2, dimX, dimY, dimZ, sigma); + + /*Projection onto convex set for P*/ + ProjP_2D(P1, P2, dimX, dimY, dimZ, alpha1); + + /* Calculate Dual Variable Q */ + DualQ_2D(V1, V2, Q1, Q2, Q3, dimX, dimY, dimZ, sigma); + + /*Projection onto convex set for Q*/ + ProjQ_2D(Q1, Q2, Q3, dimX, dimY, dimZ, alpha0); + + /*saving U into U_old*/ + copyIm(U, U_old, dimX, dimY, dimZ); + + /*adjoint operation -> divergence and projection of P*/ + DivProjP_2D(U, A, P1, P2, dimX, dimY, dimZ, lambda, tau); + + /*get updated solution U*/ + newU(U, U_old, dimX, dimY, dimZ); + + /*saving V into V_old*/ + copyIm(V1, V1_old, dimX, dimY, dimZ); + copyIm(V2, V2_old, dimX, dimY, dimZ); + + /* upd V*/ + UpdV_2D(V1, V2, P1, P2, Q1, Q2, Q3, dimX, dimY, dimZ, tau); + + /*get new V*/ + newU(V1, V1_old, dimX, dimY, dimZ); + newU(V2, V2_old, dimX, dimY, dimZ); + } /*end of iterations*/ + } + else if (number_of_dims == 3) { + mexErrMsgTxt("The input data should be a 2D array"); + /*3D case*/ + } + else {mexErrMsgTxt("The input data should be a 2D array");} + +} diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD_core.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD_core.c new file mode 100644 index 0000000..4139d10 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD_core.c @@ -0,0 +1,208 @@ +/* +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 Kazanteev +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 "TGV_PD_core.h" + +/* C-OMP implementation of Primal-Dual denoising method for + * Total Generilized Variation (TGV)-L2 model (2D case only) + * + * Input Parameters: + * 1. Noisy image/volume (2D) + * 2. lambda - regularization parameter + * 3. parameter to control first-order term (alpha1) + * 4. parameter to control the second-order term (alpha0) + * 5. Number of CP iterations + * + * Output: + * Filtered/regularized image + * + * Example: + * figure; + * Im = double(imread('lena_gray_256.tif'))/255; % loading image + * u0 = Im + .03*randn(size(Im)); % adding noise + * tic; u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550); toc; + * + * References: + * K. Bredies "Total Generalized Variation" + * + * 28.11.16/Harwell + */ + + + + +/*Calculating dual variable P (using forward differences)*/ +float DualP_2D(float *U, float *V1, float *V2, float *P1, float *P2, int dimX, int dimY, int dimZ, float sigma) +{ + int i,j; +#pragma omp parallel for shared(U,V1,V2,P1,P2) private(i,j) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + /* symmetric boundary conditions (Neuman) */ + if (i == dimX-1) P1[i*dimY + (j)] = P1[i*dimY + (j)] + sigma*((U[(i-1)*dimY + (j)] - U[i*dimY + (j)]) - V1[i*dimY + (j)]); + else P1[i*dimY + (j)] = P1[i*dimY + (j)] + sigma*((U[(i + 1)*dimY + (j)] - U[i*dimY + (j)]) - V1[i*dimY + (j)]); + if (j == dimY-1) P2[i*dimY + (j)] = P2[i*dimY + (j)] + sigma*((U[(i)*dimY + (j-1)] - U[i*dimY + (j)]) - V2[i*dimY + (j)]); + else P2[i*dimY + (j)] = P2[i*dimY + (j)] + sigma*((U[(i)*dimY + (j+1)] - U[i*dimY + (j)]) - V2[i*dimY + (j)]); + }} + return 1; +} +/*Projection onto convex set for P*/ +float ProjP_2D(float *P1, float *P2, int dimX, int dimY, int dimZ, float alpha1) +{ + float grad_magn; + int i,j; +#pragma omp parallel for shared(P1,P2) private(i,j,grad_magn) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + grad_magn = sqrt(pow(P1[i*dimY + (j)],2) + pow(P2[i*dimY + (j)],2)); + grad_magn = grad_magn/alpha1; + if (grad_magn > 1.0) { + P1[i*dimY + (j)] = P1[i*dimY + (j)]/grad_magn; + P2[i*dimY + (j)] = P2[i*dimY + (j)]/grad_magn; + } + }} + return 1; +} +/*Calculating dual variable Q (using forward differences)*/ +float DualQ_2D(float *V1, float *V2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float sigma) +{ + int i,j; + float q1, q2, q11, q22; +#pragma omp parallel for shared(Q1,Q2,Q3,V1,V2) private(i,j,q1,q2,q11,q22) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + /* symmetric boundary conditions (Neuman) */ + if (i == dimX-1) + { q1 = (V1[(i-1)*dimY + (j)] - V1[i*dimY + (j)]); + q11 = (V2[(i-1)*dimY + (j)] - V2[i*dimY + (j)]); + } + else { + q1 = (V1[(i+1)*dimY + (j)] - V1[i*dimY + (j)]); + q11 = (V2[(i+1)*dimY + (j)] - V2[i*dimY + (j)]); + } + if (j == dimY-1) { + q2 = (V2[(i)*dimY + (j-1)] - V2[i*dimY + (j)]); + q22 = (V1[(i)*dimY + (j-1)] - V1[i*dimY + (j)]); + } + else { + q2 = (V2[(i)*dimY + (j+1)] - V2[i*dimY + (j)]); + q22 = (V1[(i)*dimY + (j+1)] - V1[i*dimY + (j)]); + } + Q1[i*dimY + (j)] = Q1[i*dimY + (j)] + sigma*(q1); + Q2[i*dimY + (j)] = Q2[i*dimY + (j)] + sigma*(q2); + Q3[i*dimY + (j)] = Q3[i*dimY + (j)] + sigma*(0.5f*(q11 + q22)); + }} + return 1; +} + +float ProjQ_2D(float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float alpha0) +{ + float grad_magn; + int i,j; +#pragma omp parallel for shared(Q1,Q2,Q3) private(i,j,grad_magn) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + grad_magn = sqrt(pow(Q1[i*dimY + (j)],2) + pow(Q2[i*dimY + (j)],2) + 2*pow(Q3[i*dimY + (j)],2)); + grad_magn = grad_magn/alpha0; + if (grad_magn > 1.0) { + Q1[i*dimY + (j)] = Q1[i*dimY + (j)]/grad_magn; + Q2[i*dimY + (j)] = Q2[i*dimY + (j)]/grad_magn; + Q3[i*dimY + (j)] = Q3[i*dimY + (j)]/grad_magn; + } + }} + return 1; +} +/* Divergence and projection for P*/ +float DivProjP_2D(float *U, float *A, float *P1, float *P2, int dimX, int dimY, int dimZ, float lambda, float tau) +{ + int i,j; + float P_v1, P_v2, div; +#pragma omp parallel for shared(U,A,P1,P2) private(i,j,P_v1,P_v2,div) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + if (i == 0) P_v1 = (P1[i*dimY + (j)]); + else P_v1 = (P1[i*dimY + (j)] - P1[(i-1)*dimY + (j)]); + if (j == 0) P_v2 = (P2[i*dimY + (j)]); + else P_v2 = (P2[i*dimY + (j)] - P2[(i)*dimY + (j-1)]); + div = P_v1 + P_v2; + U[i*dimY + (j)] = (lambda*(U[i*dimY + (j)] + tau*div) + tau*A[i*dimY + (j)])/(lambda + tau); + }} + return *U; +} +/*get updated solution U*/ +float newU(float *U, float *U_old, int dimX, int dimY, int dimZ) +{ + int i; +#pragma omp parallel for shared(U,U_old) private(i) + for(i=0; i<dimX*dimY*dimZ; i++) U[i] = 2*U[i] - U_old[i]; + return *U; +} + +/*get update for V*/ +float UpdV_2D(float *V1, float *V2, float *P1, float *P2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float tau) +{ + int i,j; + float q1, q11, q2, q22, div1, div2; +#pragma omp parallel for shared(V1,V2,P1,P2,Q1,Q2,Q3) private(i,j, q1, q11, q2, q22, div1, div2) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + /* symmetric boundary conditions (Neuman) */ + if (i == 0) { + q1 = (Q1[i*dimY + (j)]); + q11 = (Q3[i*dimY + (j)]); + } + else { + q1 = (Q1[i*dimY + (j)] - Q1[(i-1)*dimY + (j)]); + q11 = (Q3[i*dimY + (j)] - Q3[(i-1)*dimY + (j)]); + } + if (j == 0) { + q2 = (Q2[i*dimY + (j)]); + q22 = (Q3[i*dimY + (j)]); + } + else { + q2 = (Q2[i*dimY + (j)] - Q2[(i)*dimY + (j-1)]); + q22 = (Q3[i*dimY + (j)] - Q3[(i)*dimY + (j-1)]); + } + div1 = q1 + q22; + div2 = q2 + q11; + V1[i*dimY + (j)] = V1[i*dimY + (j)] + tau*(P1[i*dimY + (j)] + div1); + V2[i*dimY + (j)] = V2[i*dimY + (j)] + tau*(P2[i*dimY + (j)] + div2); + }} + return 1; +} +/*********************3D *********************/ + +/*Calculating dual variable P (using forward differences)*/ +float DualP_3D(float *U, float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ, float sigma) +{ + int i,j,k; +#pragma omp parallel for shared(U,V1,V2,V3,P1,P2,P3) private(i,j,k) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + for(k=0; k<dimZ; k++) { + /* symmetric boundary conditions (Neuman) */ + if (i == dimX-1) P1[dimX*dimY*k + i*dimY + (j)] = P1[dimX*dimY*k + i*dimY + (j)] + sigma*((U[dimX*dimY*k + (i-1)*dimY + (j)] - U[dimX*dimY*k + i*dimY + (j)]) - V1[dimX*dimY*k + i*dimY + (j)]); + else P1[dimX*dimY*k + i*dimY + (j)] = P1[dimX*dimY*k + i*dimY + (j)] + sigma*((U[dimX*dimY*k + (i + 1)*dimY + (j)] - U[dimX*dimY*k + i*dimY + (j)]) - V1[dimX*dimY*k + i*dimY + (j)]); + if (j == dimY-1) P2[dimX*dimY*k + i*dimY + (j)] = P2[dimX*dimY*k + i*dimY + (j)] + sigma*((U[dimX*dimY*k + (i)*dimY + (j-1)] - U[dimX*dimY*k + i*dimY + (j)]) - V2[dimX*dimY*k + i*dimY + (j)]); + else P2[dimX*dimY*k + i*dimY + (j)] = P2[dimX*dimY*k + i*dimY + (j)] + sigma*((U[dimX*dimY*k + (i)*dimY + (j+1)] - U[dimX*dimY*k + i*dimY + (j)]) - V2[dimX*dimY*k + i*dimY + (j)]); + if (k == dimZ-1) P3[dimX*dimY*k + i*dimY + (j)] = P3[dimX*dimY*k + i*dimY + (j)] + sigma*((U[dimX*dimY*(k-1) + (i)*dimY + (j)] - U[dimX*dimY*k + i*dimY + (j)]) - V3[dimX*dimY*k + i*dimY + (j)]); + else P3[dimX*dimY*k + i*dimY + (j)] = P3[dimX*dimY*k + i*dimY + (j)] + sigma*((U[dimX*dimY*(k+1) + (i)*dimY + (j)] - U[dimX*dimY*k + i*dimY + (j)]) - V3[dimX*dimY*k + i*dimY + (j)]); + }}} + return 1; +}
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD_core.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD_core.h new file mode 100644 index 0000000..d5378df --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD_core.h @@ -0,0 +1,67 @@ +/* +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 <math.h> +#include <stdlib.h> +#include <memory.h> +#include <stdio.h> +#include "omp.h" +#include "utils.h" + +/* C-OMP implementation of Primal-Dual denoising method for +* Total Generilized Variation (TGV)-L2 model (2D case only) +* +* Input Parameters: +* 1. Noisy image/volume (2D) +* 2. lambda - regularization parameter +* 3. parameter to control first-order term (alpha1) +* 4. parameter to control the second-order term (alpha0) +* 5. Number of CP iterations +* +* Output: +* Filtered/regularized image +* +* Example: +* figure; +* Im = double(imread('lena_gray_256.tif'))/255; % loading image +* u0 = Im + .03*randn(size(Im)); % adding noise +* tic; u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550); toc; +* +* to compile with OMP support: mex TGV_PD.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +* References: +* K. Bredies "Total Generalized Variation" +* +* 28.11.16/Harwell +*/ +#ifdef __cplusplus +extern "C" { +#endif +/* 2D functions */ +float DualP_2D(float *U, float *V1, float *V2, float *P1, float *P2, int dimX, int dimY, int dimZ, float sigma); +float ProjP_2D(float *P1, float *P2, int dimX, int dimY, int dimZ, float alpha1); +float DualQ_2D(float *V1, float *V2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float sigma); +float ProjQ_2D(float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float alpha0); +float DivProjP_2D(float *U, float *A, float *P1, float *P2, int dimX, int dimY, int dimZ, float lambda, float tau); +float UpdV_2D(float *V1, float *V2, float *P1, float *P2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float tau); +float newU(float *U, float *U_old, int dimX, int dimY, int dimZ); +//float copyIm(float *A, float *U, int dimX, int dimY, int dimZ); +#ifdef __cplusplus +} +#endif diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/utils.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/utils.c new file mode 100644 index 0000000..0e83d2c --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/utils.c @@ -0,0 +1,29 @@ +/* +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 Kazanteev +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 "utils.h" + +/* Copy Image */ +float copyIm(float *A, float *U, int dimX, int dimY, int dimZ) +{ + int j; +#pragma omp parallel for shared(A, U) private(j) + for (j = 0; j<dimX*dimY*dimZ; j++) U[j] = A[j]; + return *U; +}
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/utils.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/utils.h new file mode 100644 index 0000000..53463a3 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/utils.h @@ -0,0 +1,32 @@ +/* +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 <math.h> +#include <stdlib.h> +#include <memory.h> +//#include <stdio.h> +#include "omp.h" +#ifdef __cplusplus +extern "C" { +#endif +float copyIm(float *A, float *U, int dimX, int dimY, int dimZ); +#ifdef __cplusplus +} +#endif diff --git a/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp new file mode 100644 index 0000000..5a8c7c0 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp @@ -0,0 +1,114 @@ +#include "mex.h"
+#include <matrix.h>
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include <iostream>
+#include "Diff4th_GPU_kernel.h"
+
+/*
+ * 2D and 3D CUDA implementation of the 4th order PDE denoising model by Hajiaboli
+ *
+ * Reference :
+ * "An anisotropic fourth-order diffusion filter for image noise removal" by M. Hajiaboli
+ *
+ * Example
+ * figure;
+ * Im = double(imread('lena_gray_256.tif'))/255; % loading image
+ * u0 = Im + .05*randn(size(Im)); % adding noise
+ * u = Diff4thHajiaboli_GPU(single(u0), 0.02, 150);
+ * subplot (1,2,1); imshow(u0,[ ]); title('Noisy Image')
+ * subplot (1,2,2); imshow(u,[ ]); title('Denoised Image')
+ *
+ *
+ * Linux/Matlab compilation:
+ * compile in terminal: nvcc -Xcompiler -fPIC -shared -o Diff4th_GPU_kernel.o Diff4th_GPU_kernel.cu
+ * then compile in Matlab: mex -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart Diff4thHajiaboli_GPU.cpp Diff4th_GPU_kernel.o
+ */
+
+void mexFunction(
+ int nlhs, mxArray *plhs[],
+ int nrhs, const mxArray *prhs[])
+{
+ int numdims, dimZ, size;
+ float *A, *B, *A_L, *B_L;
+ const int *dims;
+
+ numdims = mxGetNumberOfDimensions(prhs[0]);
+ dims = mxGetDimensions(prhs[0]);
+
+ float sigma = (float)mxGetScalar(prhs[1]); /* edge-preserving parameter */
+ float lambda = (float)mxGetScalar(prhs[2]); /* regularization parameter */
+ int iter = (int)mxGetScalar(prhs[3]); /* iterations number */
+
+ if (numdims == 2) {
+
+ int N, M, Z, i, j;
+ Z = 0; // for the 2D case
+ float tau = 0.01; // time step is sufficiently small for an explicit methods
+
+ /*Input data*/
+ A = (float*)mxGetData(prhs[0]);
+ N = dims[0] + 2;
+ M = dims[1] + 2;
+ A_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
+ B_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
+
+ /*Output data*/
+ B = (float*)mxGetData(plhs[0] = mxCreateNumericMatrix(dims[0], dims[1], mxSINGLE_CLASS, mxREAL));
+
+ // copy A to the bigger A_L with boundaries
+ #pragma omp parallel for shared(A_L, A) private(i,j)
+ for (i=0; i < N; i++) {
+ for (j=0; j < M; j++) {
+ if (((i > 0) && (i < N-1)) && ((j > 0) && (j < M-1))) A_L[i*M+j] = A[(i-1)*(dims[1])+(j-1)];
+ }}
+
+ // Running CUDA code here
+ Diff4th_GPU_kernel(A_L, B_L, N, M, Z, (float)sigma, iter, (float)tau, lambda);
+
+ // copy the processed B_L to a smaller B
+ #pragma omp parallel for shared(B_L, B) private(i,j)
+ for (i=0; i < N; i++) {
+ for (j=0; j < M; j++) {
+ if (((i > 0) && (i < N-1)) && ((j > 0) && (j < M-1))) B[(i-1)*(dims[1])+(j-1)] = B_L[i*M+j];
+ }}
+ }
+ if (numdims == 3) {
+ // 3D image denoising / regularization
+ int N, M, Z, i, j, k;
+ float tau = 0.0007; // Time Step is small for an explicit methods
+ A = (float*)mxGetData(prhs[0]);
+ N = dims[0] + 2;
+ M = dims[1] + 2;
+ Z = dims[2] + 2;
+ int N_dims[] = {N, M, Z};
+ A_L = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
+ B_L = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
+
+ /* output data */
+ B = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dims, mxSINGLE_CLASS, mxREAL));
+
+ // copy A to the bigger A_L with boundaries
+ #pragma omp parallel for shared(A_L, A) private(i,j,k)
+ for (i=0; i < N; i++) {
+ for (j=0; j < M; j++) {
+ for (k=0; k < Z; k++) {
+ if (((i > 0) && (i < N-1)) && ((j > 0) && (j < M-1)) && ((k > 0) && (k < Z-1))) {
+ A_L[(N*M)*(k)+(i)*M+(j)] = A[(dims[0]*dims[1])*(k-1)+(i-1)*dims[1]+(j-1)];
+ }}}}
+
+ // Running CUDA kernel here for diffusivity
+ Diff4th_GPU_kernel(A_L, B_L, N, M, Z, (float)sigma, iter, (float)tau, lambda);
+
+ // copy the processed B_L to a smaller B
+ #pragma omp parallel for shared(B_L, B) private(i,j,k)
+ for (i=0; i < N; i++) {
+ for (j=0; j < M; j++) {
+ for (k=0; k < Z; k++) {
+ if (((i > 0) && (i < N-1)) && ((j > 0) && (j < M-1)) && ((k > 0) && (k < Z-1))) {
+ B[(dims[0]*dims[1])*(k-1)+(i-1)*dims[1]+(j-1)] = B_L[(N*M)*(k)+(i)*M+(j)];
+ }}}}
+ }
+}
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.cu b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.cu new file mode 100644 index 0000000..178af00 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.cu @@ -0,0 +1,270 @@ +#include <stdio.h>
+#include <stdlib.h>
+#include <memory.h>
+#include "Diff4th_GPU_kernel.h"
+
+#define checkCudaErrors(err) __checkCudaErrors (err, __FILE__, __LINE__)
+
+inline void __checkCudaErrors(cudaError err, const char *file, const int line)
+{
+ if (cudaSuccess != err)
+ {
+ fprintf(stderr, "%s(%i) : CUDA Runtime API error %d: %s.\n",
+ file, line, (int)err, cudaGetErrorString(err));
+ exit(EXIT_FAILURE);
+ }
+}
+
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+#define sizeT (sizeX*sizeY*sizeZ)
+#define epsilon 0.00000001
+
+/////////////////////////////////////////////////
+// 2D Image denosing - Second Step (The second derrivative)
+__global__ void Diff4th2D_derriv(float* B, float* A, float *A0, int N, int M, float sigma, int iter, float tau, float lambda)
+{
+ float gradXXc = 0, gradYYc = 0;
+ int i = blockIdx.x*blockDim.x + threadIdx.x;
+ int j = blockIdx.y*blockDim.y + threadIdx.y;
+
+ int index = j + i*N;
+
+ if (((i < 1) || (i > N-2)) || ((j < 1) || (j > M-2))) {
+ return; }
+
+ int indexN = (j)+(i-1)*(N); if (A[indexN] == 0) indexN = index;
+ int indexS = (j)+(i+1)*(N); if (A[indexS] == 0) indexS = index;
+ int indexW = (j-1)+(i)*(N); if (A[indexW] == 0) indexW = index;
+ int indexE = (j+1)+(i)*(N); if (A[indexE] == 0) indexE = index;
+
+ gradXXc = B[indexN] + B[indexS] - 2*B[index] ;
+ gradYYc = B[indexW] + B[indexE] - 2*B[index] ;
+ A[index] = A[index] - tau*((A[index] - A0[index]) + lambda*(gradXXc + gradYYc));
+}
+
+// 2D Image denosing - The First Step
+__global__ void Diff4th2D(float* A, float* B, int N, int M, float sigma, int iter, float tau)
+{
+ float gradX, gradX_sq, gradY, gradY_sq, gradXX, gradYY, gradXY, sq_sum, xy_2, V_norm, V_orth, c, c_sq;
+
+ int i = blockIdx.x*blockDim.x + threadIdx.x;
+ int j = blockIdx.y*blockDim.y + threadIdx.y;
+
+ int index = j + i*N;
+
+ V_norm = 0.0f; V_orth = 0.0f;
+
+ if (((i < 1) || (i > N-2)) || ((j < 1) || (j > M-2))) {
+ return; }
+
+ int indexN = (j)+(i-1)*(N); if (A[indexN] == 0) indexN = index;
+ int indexS = (j)+(i+1)*(N); if (A[indexS] == 0) indexS = index;
+ int indexW = (j-1)+(i)*(N); if (A[indexW] == 0) indexW = index;
+ int indexE = (j+1)+(i)*(N); if (A[indexE] == 0) indexE = index;
+ int indexNW = (j-1)+(i-1)*(N); if (A[indexNW] == 0) indexNW = index;
+ int indexNE = (j+1)+(i-1)*(N); if (A[indexNE] == 0) indexNE = index;
+ int indexWS = (j-1)+(i+1)*(N); if (A[indexWS] == 0) indexWS = index;
+ int indexES = (j+1)+(i+1)*(N); if (A[indexES] == 0) indexES = index;
+
+ gradX = 0.5f*(A[indexN]-A[indexS]);
+ gradX_sq = gradX*gradX;
+ gradXX = A[indexN] + A[indexS] - 2*A[index];
+
+ gradY = 0.5f*(A[indexW]-A[indexE]);
+ gradY_sq = gradY*gradY;
+ gradYY = A[indexW] + A[indexE] - 2*A[index];
+
+ gradXY = 0.25f*(A[indexNW] - A[indexNE] - A[indexWS] + A[indexES]);
+ xy_2 = 2.0f*gradX*gradY*gradXY;
+ sq_sum = gradX_sq + gradY_sq;
+
+ if (sq_sum <= epsilon) {
+ V_norm = (gradXX*gradX_sq + xy_2 + gradYY*gradY_sq)/epsilon;
+ V_orth = (gradXX*gradY_sq - xy_2 + gradYY*gradX_sq)/epsilon; }
+ else {
+ V_norm = (gradXX*gradX_sq + xy_2 + gradYY*gradY_sq)/sq_sum;
+ V_orth = (gradXX*gradY_sq - xy_2 + gradYY*gradX_sq)/sq_sum; }
+
+ c = 1.0f/(1.0f + sq_sum/sigma);
+ c_sq = c*c;
+ B[index] = c_sq*V_norm + c*V_orth;
+}
+
+/////////////////////////////////////////////////
+// 3D data parocerssing
+__global__ void Diff4th3D_derriv(float *B, float *A, float *A0, int N, int M, int Z, float sigma, int iter, float tau, float lambda)
+{
+ float gradXXc = 0, gradYYc = 0, gradZZc = 0;
+ int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+ int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+ int zIndex = blockDim.z * blockIdx.z + threadIdx.z;
+
+ int index = xIndex + M*yIndex + N*M*zIndex;
+
+ if (((xIndex < 1) || (xIndex > N-2)) || ((yIndex < 1) || (yIndex > M-2)) || ((zIndex < 1) || (zIndex > Z-2))) {
+ return; }
+
+ int indexN = (xIndex-1) + M*yIndex + N*M*zIndex; if (A[indexN] == 0) indexN = index;
+ int indexS = (xIndex+1) + M*yIndex + N*M*zIndex; if (A[indexS] == 0) indexS = index;
+ int indexW = xIndex + M*(yIndex-1) + N*M*zIndex; if (A[indexW] == 0) indexW = index;
+ int indexE = xIndex + M*(yIndex+1) + N*M*zIndex; if (A[indexE] == 0) indexE = index;
+ int indexU = xIndex + M*yIndex + N*M*(zIndex-1); if (A[indexU] == 0) indexU = index;
+ int indexD = xIndex + M*yIndex + N*M*(zIndex+1); if (A[indexD] == 0) indexD = index;
+
+ gradXXc = B[indexN] + B[indexS] - 2*B[index] ;
+ gradYYc = B[indexW] + B[indexE] - 2*B[index] ;
+ gradZZc = B[indexU] + B[indexD] - 2*B[index] ;
+
+ A[index] = A[index] - tau*((A[index] - A0[index]) + lambda*(gradXXc + gradYYc + gradZZc));
+}
+
+__global__ void Diff4th3D(float* A, float* B, int N, int M, int Z, float sigma, int iter, float tau)
+{
+ float gradX, gradX_sq, gradY, gradY_sq, gradZ, gradZ_sq, gradXX, gradYY, gradZZ, gradXY, gradXZ, gradYZ, sq_sum, xy_2, xyz_1, xyz_2, V_norm, V_orth, c, c_sq;
+
+ int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+ int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+ int zIndex = blockDim.z * blockIdx.z + threadIdx.z;
+
+ int index = xIndex + M*yIndex + N*M*zIndex;
+ V_norm = 0.0f; V_orth = 0.0f;
+
+ if (((xIndex < 1) || (xIndex > N-2)) || ((yIndex < 1) || (yIndex > M-2)) || ((zIndex < 1) || (zIndex > Z-2))) {
+ return; }
+
+ B[index] = 0;
+
+ int indexN = (xIndex-1) + M*yIndex + N*M*zIndex; if (A[indexN] == 0) indexN = index;
+ int indexS = (xIndex+1) + M*yIndex + N*M*zIndex; if (A[indexS] == 0) indexS = index;
+ int indexW = xIndex + M*(yIndex-1) + N*M*zIndex; if (A[indexW] == 0) indexW = index;
+ int indexE = xIndex + M*(yIndex+1) + N*M*zIndex; if (A[indexE] == 0) indexE = index;
+ int indexU = xIndex + M*yIndex + N*M*(zIndex-1); if (A[indexU] == 0) indexU = index;
+ int indexD = xIndex + M*yIndex + N*M*(zIndex+1); if (A[indexD] == 0) indexD = index;
+
+ int indexNW = (xIndex-1) + M*(yIndex-1) + N*M*zIndex; if (A[indexNW] == 0) indexNW = index;
+ int indexNE = (xIndex-1) + M*(yIndex+1) + N*M*zIndex; if (A[indexNE] == 0) indexNE = index;
+ int indexWS = (xIndex+1) + M*(yIndex-1) + N*M*zIndex; if (A[indexWS] == 0) indexWS = index;
+ int indexES = (xIndex+1) + M*(yIndex+1) + N*M*zIndex; if (A[indexES] == 0) indexES = index;
+
+ int indexUW = (xIndex-1) + M*(yIndex) + N*M*(zIndex-1); if (A[indexUW] == 0) indexUW = index;
+ int indexUE = (xIndex+1) + M*(yIndex) + N*M*(zIndex-1); if (A[indexUE] == 0) indexUE = index;
+ int indexDW = (xIndex-1) + M*(yIndex) + N*M*(zIndex+1); if (A[indexDW] == 0) indexDW = index;
+ int indexDE = (xIndex+1) + M*(yIndex) + N*M*(zIndex+1); if (A[indexDE] == 0) indexDE = index;
+
+ int indexUN = (xIndex) + M*(yIndex-1) + N*M*(zIndex-1); if (A[indexUN] == 0) indexUN = index;
+ int indexUS = (xIndex) + M*(yIndex+1) + N*M*(zIndex-1); if (A[indexUS] == 0) indexUS = index;
+ int indexDN = (xIndex) + M*(yIndex-1) + N*M*(zIndex+1); if (A[indexDN] == 0) indexDN = index;
+ int indexDS = (xIndex) + M*(yIndex+1) + N*M*(zIndex+1); if (A[indexDS] == 0) indexDS = index;
+
+ gradX = 0.5f*(A[indexN]-A[indexS]);
+ gradX_sq = gradX*gradX;
+ gradXX = A[indexN] + A[indexS] - 2*A[index];
+
+ gradY = 0.5f*(A[indexW]-A[indexE]);
+ gradY_sq = gradY*gradY;
+ gradYY = A[indexW] + A[indexE] - 2*A[index];
+
+ gradZ = 0.5f*(A[indexU]-A[indexD]);
+ gradZ_sq = gradZ*gradZ;
+ gradZZ = A[indexU] + A[indexD] - 2*A[index];
+
+ gradXY = 0.25f*(A[indexNW] - A[indexNE] - A[indexWS] + A[indexES]);
+ gradXZ = 0.25f*(A[indexUW] - A[indexUE] - A[indexDW] + A[indexDE]);
+ gradYZ = 0.25f*(A[indexUN] - A[indexUS] - A[indexDN] + A[indexDS]);
+
+ xy_2 = 2.0f*gradX*gradY*gradXY;
+ xyz_1 = 2.0f*gradX*gradZ*gradXZ;
+ xyz_2 = 2.0f*gradY*gradZ*gradYZ;
+
+ sq_sum = gradX_sq + gradY_sq + gradZ_sq;
+
+ if (sq_sum <= epsilon) {
+ V_norm = (gradXX*gradX_sq + gradYY*gradY_sq + gradZZ*gradZ_sq + xy_2 + xyz_1 + xyz_2)/epsilon;
+ V_orth = ((gradY_sq + gradZ_sq)*gradXX + (gradX_sq + gradZ_sq)*gradYY + (gradX_sq + gradY_sq)*gradZZ - xy_2 - xyz_1 - xyz_2)/epsilon; }
+ else {
+ V_norm = (gradXX*gradX_sq + gradYY*gradY_sq + gradZZ*gradZ_sq + xy_2 + xyz_1 + xyz_2)/sq_sum;
+ V_orth = ((gradY_sq + gradZ_sq)*gradXX + (gradX_sq + gradZ_sq)*gradYY + (gradX_sq + gradY_sq)*gradZZ - xy_2 - xyz_1 - xyz_2)/sq_sum; }
+
+ c = 1;
+ if ((1.0f + sq_sum/sigma) != 0.0f) {c = 1.0f/(1.0f + sq_sum/sigma);}
+
+ c_sq = c*c;
+ B[index] = c_sq*V_norm + c*V_orth;
+}
+
+/******************************************************/
+/********* HOST FUNCTION*************/
+extern "C" void Diff4th_GPU_kernel(float* A, float* B, int N, int M, int Z, float sigma, int iter, float tau, float lambda)
+{
+ int deviceCount = -1; // number of devices
+ cudaGetDeviceCount(&deviceCount);
+ if (deviceCount == 0) {
+ fprintf(stderr, "No CUDA devices found\n");
+ return;
+ }
+
+ int BLKXSIZE, BLKYSIZE,BLKZSIZE;
+ float *Ad, *Bd, *Cd;
+ sigma = sigma*sigma;
+
+ if (Z == 0){
+ // 4th order diffusion for 2D case
+ BLKXSIZE = 8;
+ BLKYSIZE = 16;
+
+ dim3 dimBlock(BLKXSIZE,BLKYSIZE);
+ dim3 dimGrid(idivup(N,BLKXSIZE), idivup(M,BLKYSIZE));
+
+ checkCudaErrors(cudaMalloc((void**)&Ad,N*M*sizeof(float)));
+ checkCudaErrors(cudaMalloc((void**)&Bd,N*M*sizeof(float)));
+ checkCudaErrors(cudaMalloc((void**)&Cd,N*M*sizeof(float)));
+
+ checkCudaErrors(cudaMemcpy(Ad,A,N*M*sizeof(float),cudaMemcpyHostToDevice));
+ checkCudaErrors(cudaMemcpy(Bd,A,N*M*sizeof(float),cudaMemcpyHostToDevice));
+ checkCudaErrors(cudaMemcpy(Cd,A,N*M*sizeof(float),cudaMemcpyHostToDevice));
+
+ int n = 1;
+ while (n <= iter) {
+ Diff4th2D<<<dimGrid,dimBlock>>>(Bd, Cd, N, M, sigma, iter, tau);
+ cudaDeviceSynchronize();
+ checkCudaErrors( cudaPeekAtLastError() );
+ Diff4th2D_derriv<<<dimGrid,dimBlock>>>(Cd, Bd, Ad, N, M, sigma, iter, tau, lambda);
+ cudaDeviceSynchronize();
+ checkCudaErrors( cudaPeekAtLastError() );
+ n++;
+ }
+ checkCudaErrors(cudaMemcpy(B,Bd,N*M*sizeof(float),cudaMemcpyDeviceToHost));
+ cudaFree(Ad); cudaFree(Bd); cudaFree(Cd);
+ }
+
+ if (Z != 0){
+ // 4th order diffusion for 3D case
+ BLKXSIZE = 8;
+ BLKYSIZE = 8;
+ BLKZSIZE = 8;
+
+ dim3 dimBlock(BLKXSIZE,BLKYSIZE,BLKZSIZE);
+ dim3 dimGrid(idivup(N,BLKXSIZE), idivup(M,BLKYSIZE),idivup(Z,BLKXSIZE));
+
+ checkCudaErrors(cudaMalloc((void**)&Ad,N*M*Z*sizeof(float)));
+ checkCudaErrors(cudaMalloc((void**)&Bd,N*M*Z*sizeof(float)));
+ checkCudaErrors(cudaMalloc((void**)&Cd,N*M*Z*sizeof(float)));
+
+ checkCudaErrors(cudaMemcpy(Ad,A,N*M*Z*sizeof(float),cudaMemcpyHostToDevice));
+ checkCudaErrors(cudaMemcpy(Bd,A,N*M*Z*sizeof(float),cudaMemcpyHostToDevice));
+ checkCudaErrors(cudaMemcpy(Cd,A,N*M*Z*sizeof(float),cudaMemcpyHostToDevice));
+
+ int n = 1;
+ while (n <= iter) {
+ Diff4th3D<<<dimGrid,dimBlock>>>(Bd, Cd, N, M, Z, sigma, iter, tau);
+ cudaDeviceSynchronize();
+ checkCudaErrors( cudaPeekAtLastError() );
+ Diff4th3D_derriv<<<dimGrid,dimBlock>>>(Cd, Bd, Ad, N, M, Z, sigma, iter, tau, lambda);
+ cudaDeviceSynchronize();
+ checkCudaErrors( cudaPeekAtLastError() );
+ n++;
+ }
+ checkCudaErrors(cudaMemcpy(B,Bd,N*M*Z*sizeof(float),cudaMemcpyDeviceToHost));
+ cudaFree(Ad); cudaFree(Bd); cudaFree(Cd);
+ }
+}
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.h b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.h new file mode 100644 index 0000000..cfbb45a --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.h @@ -0,0 +1,6 @@ +#ifndef __DIFF_HO_H_ +#define __DIFF_HO_H_ + +extern "C" void Diff4th_GPU_kernel(float* A, float* B, int N, int M, int Z, float sigma, int iter, float tau, float lambda); + +#endif diff --git a/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU.cpp b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU.cpp new file mode 100644 index 0000000..ff0cc90 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU.cpp @@ -0,0 +1,171 @@ +#include "mex.h"
+#include <matrix.h>
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include <iostream>
+#include "NLM_GPU_kernel.h"
+
+/* CUDA implementation of the patch-based (PB) regularization for 2D and 3D images/volumes
+ * This method finds self-similar patches in data and performs one fixed point iteration to mimimize the PB penalty function
+ *
+ * References: 1. Yang Z. & Jacob M. "Nonlocal Regularization of Inverse Problems"
+ * 2. Kazantsev D. at. all "4D-CT reconstruction with unified spatial-temporal patch-based regularization"
+ *
+ * Input Parameters (mandatory):
+ * 1. Image/volume (2D/3D)
+ * 2. ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window)
+ * 3. ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window)
+ * 4. h - parameter for the PB penalty function
+ * 5. lambda - regularization parameter
+
+ * Output:
+ * 1. regularized (denoised) Image/volume (N x N x N)
+ *
+ * In matlab check what kind of GPU you have with "gpuDevice" command,
+ * then set your ComputeCapability, here I use -arch compute_35
+ *
+ * Quick 2D denoising example in Matlab:
+ Im = double(imread('lena_gray_256.tif'))/255; % loading image
+ u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+ ImDen = NLM_GPU(single(u0), 3, 2, 0.15, 1);
+
+ * Linux/Matlab compilation:
+ * compile in terminal: nvcc -Xcompiler -fPIC -shared -o NLM_GPU_kernel.o NLM_GPU_kernel.cu
+ * then compile in Matlab: mex -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart NLM_GPU.cpp NLM_GPU_kernel.o
+ *
+ * D. Kazantsev
+ * 2014-17
+ * Harwell/Manchester UK
+ */
+
+float pad_crop(float *A, float *Ap, int OldSizeX, int OldSizeY, int OldSizeZ, int NewSizeX, int NewSizeY, int NewSizeZ, int padXY, int switchpad_crop);
+
+void mexFunction(
+ int nlhs, mxArray *plhs[],
+ int nrhs, const mxArray *prhs[])
+{
+ int N, M, Z, i_n, j_n, k_n, numdims, SearchW, SimilW, SearchW_real, padXY, newsizeX, newsizeY, newsizeZ, switchpad_crop, count, SearchW_full, SimilW_full;
+ const int *dims;
+ float *A, *B=NULL, *Ap=NULL, *Bp=NULL, *Eucl_Vec, h, h2, lambda, val, denh2;
+
+ numdims = mxGetNumberOfDimensions(prhs[0]);
+ dims = mxGetDimensions(prhs[0]);
+
+ N = dims[0];
+ M = dims[1];
+ Z = dims[2];
+
+ if ((numdims < 2) || (numdims > 3)) {mexErrMsgTxt("The input should be 2D image or 3D volume");}
+ if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input in single precision is required"); }
+
+ if(nrhs != 5) mexErrMsgTxt("Five inputs reqired: Image(2D,3D), SearchW, SimilW, Threshold, Regularization parameter");
+
+ /*Handling inputs*/
+ A = (float *) mxGetData(prhs[0]); /* the image to regularize/filter */
+ SearchW_real = (int) mxGetScalar(prhs[1]); /* the searching window ratio */
+ SimilW = (int) mxGetScalar(prhs[2]); /* the similarity window ratio */
+ h = (float) mxGetScalar(prhs[3]); /* parameter for the PB filtering function */
+ lambda = (float) mxGetScalar(prhs[4]);
+
+ if (h <= 0) mexErrMsgTxt("Parmeter for the PB penalty function should be > 0");
+
+ SearchW = SearchW_real + 2*SimilW;
+
+ SearchW_full = 2*SearchW + 1; /* the full searching window size */
+ SimilW_full = 2*SimilW + 1; /* the full similarity window size */
+ h2 = h*h;
+
+ padXY = SearchW + 2*SimilW; /* padding sizes */
+ newsizeX = N + 2*(padXY); /* the X size of the padded array */
+ newsizeY = M + 2*(padXY); /* the Y size of the padded array */
+ newsizeZ = Z + 2*(padXY); /* the Z size of the padded array */
+ int N_dims[] = {newsizeX, newsizeY, newsizeZ};
+
+ /******************************2D case ****************************/
+ if (numdims == 2) {
+ /*Handling output*/
+ B = (float*)mxGetData(plhs[0] = mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
+ /*allocating memory for the padded arrays */
+ Ap = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
+ Bp = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
+ Eucl_Vec = (float*)mxGetData(mxCreateNumericMatrix(SimilW_full*SimilW_full, 1, mxSINGLE_CLASS, mxREAL));
+
+ /*Gaussian kernel */
+ count = 0;
+ for(i_n=-SimilW; i_n<=SimilW; i_n++) {
+ for(j_n=-SimilW; j_n<=SimilW; j_n++) {
+ val = (float)(i_n*i_n + j_n*j_n)/(2*SimilW*SimilW);
+ Eucl_Vec[count] = exp(-val);
+ count = count + 1;
+ }} /*main neighb loop */
+
+ /**************************************************************************/
+ /*Perform padding of image A to the size of [newsizeX * newsizeY] */
+ switchpad_crop = 0; /*padding*/
+ pad_crop(A, Ap, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
+
+ /* Do PB regularization with the padded array */
+ NLM_GPU_kernel(Ap, Bp, Eucl_Vec, newsizeY, newsizeX, 0, numdims, SearchW, SimilW, SearchW_real, (float)h2, (float)lambda);
+
+ switchpad_crop = 1; /*cropping*/
+ pad_crop(Bp, B, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
+ }
+ else
+ {
+ /******************************3D case ****************************/
+ /*Handling output*/
+ B = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dims, mxSINGLE_CLASS, mxREAL));
+ /*allocating memory for the padded arrays */
+ Ap = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
+ Bp = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
+ Eucl_Vec = (float*)mxGetData(mxCreateNumericMatrix(SimilW_full*SimilW_full*SimilW_full, 1, mxSINGLE_CLASS, mxREAL));
+
+ /*Gaussian kernel */
+ count = 0;
+ for(i_n=-SimilW; i_n<=SimilW; i_n++) {
+ for(j_n=-SimilW; j_n<=SimilW; j_n++) {
+ for(k_n=-SimilW; k_n<=SimilW; k_n++) {
+ val = (float)(i_n*i_n + j_n*j_n + k_n*k_n)/(2*SimilW*SimilW*SimilW);
+ Eucl_Vec[count] = exp(-val);
+ count = count + 1;
+ }}} /*main neighb loop */
+ /**************************************************************************/
+ /*Perform padding of image A to the size of [newsizeX * newsizeY * newsizeZ] */
+ switchpad_crop = 0; /*padding*/
+ pad_crop(A, Ap, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
+
+ /* Do PB regularization with the padded array */
+ NLM_GPU_kernel(Ap, Bp, Eucl_Vec, newsizeY, newsizeX, newsizeZ, numdims, SearchW, SimilW, SearchW_real, (float)h2, (float)lambda);
+
+ switchpad_crop = 1; /*cropping*/
+ pad_crop(Bp, B, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
+ } /*end else ndims*/
+}
+
+float pad_crop(float *A, float *Ap, int OldSizeX, int OldSizeY, int OldSizeZ, int NewSizeX, int NewSizeY, int NewSizeZ, int padXY, int switchpad_crop)
+{
+ /* padding-cropping function */
+ int i,j,k;
+ if (NewSizeZ > 1) {
+ for (i=0; i < NewSizeX; i++) {
+ for (j=0; j < NewSizeY; j++) {
+ for (k=0; k < NewSizeZ; k++) {
+ if (((i >= padXY) && (i < NewSizeX-padXY)) && ((j >= padXY) && (j < NewSizeY-padXY)) && ((k >= padXY) && (k < NewSizeZ-padXY))) {
+ if (switchpad_crop == 0) Ap[NewSizeX*NewSizeY*k + i*NewSizeY+j] = A[OldSizeX*OldSizeY*(k - padXY) + (i-padXY)*(OldSizeY)+(j-padXY)];
+ else Ap[OldSizeX*OldSizeY*(k - padXY) + (i-padXY)*(OldSizeY)+(j-padXY)] = A[NewSizeX*NewSizeY*k + i*NewSizeY+j];
+ }
+ }}}
+ }
+ else {
+ for (i=0; i < NewSizeX; i++) {
+ for (j=0; j < NewSizeY; j++) {
+ if (((i >= padXY) && (i < NewSizeX-padXY)) && ((j >= padXY) && (j < NewSizeY-padXY))) {
+ if (switchpad_crop == 0) Ap[i*NewSizeY+j] = A[(i-padXY)*(OldSizeY)+(j-padXY)];
+ else Ap[(i-padXY)*(OldSizeY)+(j-padXY)] = A[i*NewSizeY+j];
+ }
+ }}
+ }
+ return *Ap;
+}
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU_kernel.cu b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU_kernel.cu new file mode 100644 index 0000000..17da3a8 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU_kernel.cu @@ -0,0 +1,239 @@ +#include <stdio.h>
+#include <stdlib.h>
+#include <memory.h>
+#include "NLM_GPU_kernel.h"
+
+#define checkCudaErrors(err) __checkCudaErrors (err, __FILE__, __LINE__)
+
+inline void __checkCudaErrors(cudaError err, const char *file, const int line)
+{
+ if (cudaSuccess != err)
+ {
+ fprintf(stderr, "%s(%i) : CUDA Runtime API error %d: %s.\n",
+ file, line, (int)err, cudaGetErrorString(err));
+ exit(EXIT_FAILURE);
+ }
+}
+
+extern __shared__ float sharedmem[];
+
+// run PB den kernel here
+__global__ void NLM_kernel(float *Ad, float* Bd, float *Eucl_Vec_d, int N, int M, int Z, int SearchW, int SimilW, int SearchW_real, int SearchW_full, int SimilW_full, int padXY, float h2, float lambda, dim3 imagedim, dim3 griddim, dim3 kerneldim, dim3 sharedmemdim, int nUpdatePerThread, float neighborsize)
+{
+
+ int i1, j1, k1, i2, j2, k2, i3, j3, k3, i_l, j_l, k_l, count;
+ float value, Weight_norm, normsum, Weight;
+
+ int bidx = blockIdx.x;
+ int bidy = blockIdx.y%griddim.y;
+ int bidz = (int)((blockIdx.y)/griddim.y);
+
+ // global index for block endpoint
+ int beidx = __mul24(bidx,blockDim.x);
+ int beidy = __mul24(bidy,blockDim.y);
+ int beidz = __mul24(bidz,blockDim.z);
+
+ int tid = __mul24(threadIdx.z,__mul24(blockDim.x,blockDim.y)) +
+ __mul24(threadIdx.y,blockDim.x) + threadIdx.x;
+
+ #ifdef __DEVICE_EMULATION__
+ printf("tid : %d", tid);
+ #endif
+
+ // update shared memory
+ int nthreads = blockDim.x*blockDim.y*blockDim.z;
+ int sharedMemSize = sharedmemdim.x * sharedmemdim.y * sharedmemdim.z;
+ for(int i=0; i<nUpdatePerThread; i++)
+ {
+ int sid = tid + i*nthreads; // index in shared memory
+ if (sid < sharedMemSize)
+ {
+ // global x/y/z index in volume
+ int gidx, gidy, gidz;
+ int sidx, sidy, sidz, tid;
+
+ sidz = sid / (sharedmemdim.x*sharedmemdim.y);
+ tid = sid - sidz*(sharedmemdim.x*sharedmemdim.y);
+ sidy = tid / (sharedmemdim.x);
+ sidx = tid - sidy*(sharedmemdim.x);
+
+ gidx = (int)sidx - (int)kerneldim.x + (int)beidx;
+ gidy = (int)sidy - (int)kerneldim.y + (int)beidy;
+ gidz = (int)sidz - (int)kerneldim.z + (int)beidz;
+
+ // Neumann boundary condition
+ int cx = (int) min(max(0,gidx),imagedim.x-1);
+ int cy = (int) min(max(0,gidy),imagedim.y-1);
+ int cz = (int) min(max(0,gidz),imagedim.z-1);
+
+ int gid = cz*imagedim.x*imagedim.y + cy*imagedim.x + cx;
+
+ sharedmem[sid] = Ad[gid];
+ }
+ }
+ __syncthreads();
+
+ // global index of the current voxel in the input volume
+ int idx = beidx + threadIdx.x;
+ int idy = beidy + threadIdx.y;
+ int idz = beidz + threadIdx.z;
+
+ if (Z == 1) {
+ /* 2D case */
+ /*checking boundaries to be within the image and avoid padded spaces */
+ if( idx >= padXY && idx < (imagedim.x - padXY) &&
+ idy >= padXY && idy < (imagedim.y - padXY))
+ {
+ int i_centr = threadIdx.x + (SearchW); /*indices of the centrilized (main) pixel */
+ int j_centr = threadIdx.y + (SearchW); /*indices of the centrilized (main) pixel */
+
+ if ((i_centr > 0) && (i_centr < N) && (j_centr > 0) && (j_centr < M)) {
+
+ Weight_norm = 0; value = 0.0;
+ /* Massive Search window loop */
+ for(i1 = i_centr - SearchW_real ; i1 <= i_centr + SearchW_real; i1++) {
+ for(j1 = j_centr - SearchW_real ; j1<= j_centr + SearchW_real ; j1++) {
+ /* if inside the searching window */
+ count = 0; normsum = 0.0;
+ for(i_l=-SimilW; i_l<=SimilW; i_l++) {
+ for(j_l=-SimilW; j_l<=SimilW; j_l++) {
+ i2 = i1+i_l; j2 = j1+j_l;
+ i3 = i_centr+i_l; j3 = j_centr+j_l; /*coordinates of the inner patch loop */
+ if ((i2 > 0) && (i2 < N) && (j2 > 0) && (j2 < M)) {
+ if ((i3 > 0) && (i3 < N) && (j3 > 0) && (j3 < M)) {
+ normsum += Eucl_Vec_d[count]*pow((sharedmem[(j3)*sharedmemdim.x+(i3)] - sharedmem[j2*sharedmemdim.x+i2]), 2);
+ }}
+ count++;
+ }}
+ if (normsum != 0) Weight = (expf(-normsum/h2));
+ else Weight = 0.0;
+ Weight_norm += Weight;
+ value += sharedmem[j1*sharedmemdim.x+i1]*Weight;
+ }}
+
+ if (Weight_norm != 0) Bd[idz*imagedim.x*imagedim.y + idy*imagedim.x + idx] = value/Weight_norm;
+ else Bd[idz*imagedim.x*imagedim.y + idy*imagedim.x + idx] = Ad[idz*imagedim.x*imagedim.y + idy*imagedim.x + idx];
+ }
+ } /*boundary conditions end*/
+ }
+ else {
+ /*3D case*/
+ /*checking boundaries to be within the image and avoid padded spaces */
+ if( idx >= padXY && idx < (imagedim.x - padXY) &&
+ idy >= padXY && idy < (imagedim.y - padXY) &&
+ idz >= padXY && idz < (imagedim.z - padXY) )
+ {
+ int i_centr = threadIdx.x + SearchW; /*indices of the centrilized (main) pixel */
+ int j_centr = threadIdx.y + SearchW; /*indices of the centrilized (main) pixel */
+ int k_centr = threadIdx.z + SearchW; /*indices of the centrilized (main) pixel */
+
+ if ((i_centr > 0) && (i_centr < N) && (j_centr > 0) && (j_centr < M) && (k_centr > 0) && (k_centr < Z)) {
+
+ Weight_norm = 0; value = 0.0;
+ /* Massive Search window loop */
+ for(i1 = i_centr - SearchW_real ; i1 <= i_centr + SearchW_real; i1++) {
+ for(j1 = j_centr - SearchW_real ; j1<= j_centr + SearchW_real ; j1++) {
+ for(k1 = k_centr - SearchW_real ; k1<= k_centr + SearchW_real ; k1++) {
+ /* if inside the searching window */
+ count = 0; normsum = 0.0;
+ for(i_l=-SimilW; i_l<=SimilW; i_l++) {
+ for(j_l=-SimilW; j_l<=SimilW; j_l++) {
+ for(k_l=-SimilW; k_l<=SimilW; k_l++) {
+ i2 = i1+i_l; j2 = j1+j_l; k2 = k1+k_l;
+ i3 = i_centr+i_l; j3 = j_centr+j_l; k3 = k_centr+k_l; /*coordinates of the inner patch loop */
+ if ((i2 > 0) && (i2 < N) && (j2 > 0) && (j2 < M) && (k2 > 0) && (k2 < Z)) {
+ if ((i3 > 0) && (i3 < N) && (j3 > 0) && (j3 < M) && (k3 > 0) && (k3 < Z)) {
+ normsum += Eucl_Vec_d[count]*pow((sharedmem[(k3)*sharedmemdim.x*sharedmemdim.y + (j3)*sharedmemdim.x+(i3)] - sharedmem[(k2)*sharedmemdim.x*sharedmemdim.y + j2*sharedmemdim.x+i2]), 2);
+ }}
+ count++;
+ }}}
+ if (normsum != 0) Weight = (expf(-normsum/h2));
+ else Weight = 0.0;
+ Weight_norm += Weight;
+ value += sharedmem[k1*sharedmemdim.x*sharedmemdim.y + j1*sharedmemdim.x+i1]*Weight;
+ }}} /* BIG search window loop end*/
+
+
+ if (Weight_norm != 0) Bd[idz*imagedim.x*imagedim.y + idy*imagedim.x + idx] = value/Weight_norm;
+ else Bd[idz*imagedim.x*imagedim.y + idy*imagedim.x + idx] = Ad[idz*imagedim.x*imagedim.y + idy*imagedim.x + idx];
+ }
+ } /* boundary conditions end */
+ }
+}
+
+/////////////////////////////////////////////////
+// HOST FUNCTION
+extern "C" void NLM_GPU_kernel(float *A, float* B, float *Eucl_Vec, int N, int M, int Z, int dimension, int SearchW, int SimilW, int SearchW_real, float h2, float lambda)
+{
+ int deviceCount = -1; // number of devices
+ cudaGetDeviceCount(&deviceCount);
+ if (deviceCount == 0) {
+ fprintf(stderr, "No CUDA devices found\n");
+ return;
+ }
+
+// cudaDeviceReset();
+
+ int padXY, SearchW_full, SimilW_full, blockWidth, blockHeight, blockDepth, nBlockX, nBlockY, nBlockZ, kernel_depth;
+ float *Ad, *Bd, *Eucl_Vec_d;
+
+ if (dimension == 2) {
+ blockWidth = 16;
+ blockHeight = 16;
+ blockDepth = 1;
+ Z = 1;
+ kernel_depth = 0;
+ }
+ else {
+ blockWidth = 8;
+ blockHeight = 8;
+ blockDepth = 8;
+ kernel_depth = SearchW;
+ }
+
+ // compute how many blocks are needed
+ nBlockX = ceil((float)N / (float)blockWidth);
+ nBlockY = ceil((float)M / (float)blockHeight);
+ nBlockZ = ceil((float)Z / (float)blockDepth);
+
+ dim3 dimGrid(nBlockX,nBlockY*nBlockZ);
+ dim3 dimBlock(blockWidth, blockHeight, blockDepth);
+ dim3 imagedim(N,M,Z);
+ dim3 griddim(nBlockX,nBlockY,nBlockZ);
+
+ dim3 kerneldim(SearchW,SearchW,kernel_depth);
+ dim3 sharedmemdim((SearchW*2)+blockWidth,(SearchW*2)+blockHeight,(kernel_depth*2)+blockDepth);
+ int sharedmemsize = sizeof(float)*sharedmemdim.x*sharedmemdim.y*sharedmemdim.z;
+ int updateperthread = ceil((float)(sharedmemdim.x*sharedmemdim.y*sharedmemdim.z)/(float)(blockWidth*blockHeight*blockDepth));
+ float neighborsize = (2*SearchW+1)*(2*SearchW+1)*(2*kernel_depth+1);
+
+ padXY = SearchW + 2*SimilW; /* padding sizes */
+
+ SearchW_full = 2*SearchW + 1; /* the full searching window size */
+ SimilW_full = 2*SimilW + 1; /* the full similarity window size */
+
+ /*allocate space for images on device*/
+ checkCudaErrors( cudaMalloc((void**)&Ad,N*M*Z*sizeof(float)) );
+ checkCudaErrors( cudaMalloc((void**)&Bd,N*M*Z*sizeof(float)) );
+ /*allocate space for vectors on device*/
+ if (dimension == 2) {
+ checkCudaErrors( cudaMalloc((void**)&Eucl_Vec_d,SimilW_full*SimilW_full*sizeof(float)) );
+ checkCudaErrors( cudaMemcpy(Eucl_Vec_d,Eucl_Vec,SimilW_full*SimilW_full*sizeof(float),cudaMemcpyHostToDevice) );
+ }
+ else {
+ checkCudaErrors( cudaMalloc((void**)&Eucl_Vec_d,SimilW_full*SimilW_full*SimilW_full*sizeof(float)) );
+ checkCudaErrors( cudaMemcpy(Eucl_Vec_d,Eucl_Vec,SimilW_full*SimilW_full*SimilW_full*sizeof(float),cudaMemcpyHostToDevice) );
+ }
+
+ /* copy data from the host to device */
+ checkCudaErrors( cudaMemcpy(Ad,A,N*M*Z*sizeof(float),cudaMemcpyHostToDevice) );
+
+ // Run CUDA kernel here
+ NLM_kernel<<<dimGrid,dimBlock,sharedmemsize>>>(Ad, Bd, Eucl_Vec_d, M, N, Z, SearchW, SimilW, SearchW_real, SearchW_full, SimilW_full, padXY, h2, lambda, imagedim, griddim, kerneldim, sharedmemdim, updateperthread, neighborsize);
+
+ checkCudaErrors( cudaPeekAtLastError() );
+// gpuErrchk( cudaDeviceSynchronize() );
+
+ checkCudaErrors( cudaMemcpy(B,Bd,N*M*Z*sizeof(float),cudaMemcpyDeviceToHost) );
+ cudaFree(Ad); cudaFree(Bd); cudaFree(Eucl_Vec_d);
+}
diff --git a/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU_kernel.h b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU_kernel.h new file mode 100644 index 0000000..bc9d4a3 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU_kernel.h @@ -0,0 +1,6 @@ +#ifndef __NLMREG_KERNELS_H_ +#define __NLMREG_KERNELS_H_ + +extern "C" void NLM_GPU_kernel(float *A, float* B, float *Eucl_Vec, int N, int M, int Z, int dimension, int SearchW, int SimilW, int SearchW_real, float denh2, float lambda); + +#endif diff --git a/Wrappers/Matlab/supp/RMSE.m b/Wrappers/Matlab/supp/RMSE.m new file mode 100644 index 0000000..002f776 --- /dev/null +++ b/Wrappers/Matlab/supp/RMSE.m @@ -0,0 +1,7 @@ +function err = RMSE(signal1, signal2)
+%RMSE Root Mean Squared Error
+
+err = sum((signal1 - signal2).^2)/length(signal1); % MSE
+err = sqrt(err); % RMSE
+
+end
\ No newline at end of file diff --git a/Wrappers/Matlab/supp/my_red_yellowMAP.mat b/Wrappers/Matlab/supp/my_red_yellowMAP.mat Binary files differnew file mode 100644 index 0000000..c2a5b87 --- /dev/null +++ b/Wrappers/Matlab/supp/my_red_yellowMAP.mat diff --git a/Wrappers/Matlab/supp/sino_add_artifacts.m b/Wrappers/Matlab/supp/sino_add_artifacts.m new file mode 100644 index 0000000..f601914 --- /dev/null +++ b/Wrappers/Matlab/supp/sino_add_artifacts.m @@ -0,0 +1,33 @@ +function sino_artifacts = sino_add_artifacts(sino,artifact_type) +% function to add various distortions to the sinogram space, current +% version includes: random rings and zingers (streaks) +% Input: +% 1. sinogram +% 2. artifact type: 'rings' or 'zingers' (streaks) + + +[Detectors, anglesNumb, SlicesZ] = size(sino); +fprintf('%s %i %s %i %s %i %s \n', 'Sinogram has a dimension of', Detectors, 'detectors;', anglesNumb, 'projections;', SlicesZ, 'vertical slices.'); + +sino_artifacts = sino; + +if (strcmp(artifact_type,'rings')) + fprintf('%s \n', 'Adding rings...'); + NumRings = round(Detectors/20); % Number of rings relatively to the size of Detectors + IntenOff = linspace(0.05,0.5,NumRings); % the intensity of rings in the selected range + + for k = 1:SlicesZ + % generate random indices to propagate rings + RandInd = randperm(Detectors,Detectors); + for jj = 1:NumRings + ind_c = RandInd(jj); + sino_artifacts(ind_c,1:end,k) = sino_artifacts(ind_c,1:end,k) + IntenOff(jj).*sino_artifacts(ind_c,1:end,k); % generate a constant offset + end + + end +elseif (strcmp(artifact_type,'zingers')) + fprintf('%s \n', 'Adding zingers...'); +else + fprintf('%s \n', 'Nothing selected, the same sinogram returned...'); +end +end
\ No newline at end of file diff --git a/Wrappers/Matlab/studentst.m b/Wrappers/Matlab/supp/studentst.m index 99fed1e..99fed1e 100644 --- a/Wrappers/Matlab/studentst.m +++ b/Wrappers/Matlab/supp/studentst.m diff --git a/Wrappers/Matlab/supp/zing_rings_add.m b/Wrappers/Matlab/supp/zing_rings_add.m new file mode 100644 index 0000000..d197b1f --- /dev/null +++ b/Wrappers/Matlab/supp/zing_rings_add.m @@ -0,0 +1,91 @@ +% uncomment this part of script to generate data with different noise characterisitcs + +fprintf('%s\n', 'Generating Projection Data...'); + +% Creating RHS (b) - the sinogram (using a strip projection model) +% vol_geom = astra_create_vol_geom(N, N); +% proj_geom = astra_create_proj_geom('parallel', 1.0, P, theta_rad); +% proj_id_temp = astra_create_projector('strip', proj_geom, vol_geom); +% [sinogram_id, sinogramIdeal] = astra_create_sino(phantom, proj_id_temp); +% astra_mex_data2d('delete',sinogram_id); +% astra_mex_algorithm('delete',proj_id_temp); + +%% +% inverse crime data generation +[sino_id, sinogramIdeal] = astra_create_sino3d_cuda(phantom, proj_geom, vol_geom); +astra_mex_data3d('delete', sino_id); + +% [id,x] = astra_create_backprojection3d_cuda(sinogramIdeal, proj_geom, vol_geom); +% astra_mex_data3d('delete', id); +%% +% +% % adding Gaussian noise +% eta = 0.04; % Relative noise level +% E = randn(size(sinogram)); +% sinogram = sinogram + eta*norm(sinogram,'fro')*E/norm(E,'fro'); % adding noise to the sinogram +% sinogram(sinogram<0) = 0; +% clear E; + +%% +% adding zingers +val_offset = 0; +sino_zing = sinogramIdeal'; +vec1 = [60, 80, 80, 70, 70, 90, 90, 40, 130, 145, 155, 125]; +vec2 = [350, 450, 190, 500, 250, 530, 330, 230, 550, 250, 450, 195]; +for jj = 1:length(vec1) + for i1 = -2:2 + for j1 = -2:2 + sino_zing(vec1(jj)+i1, vec2(jj)+j1) = val_offset; + end + end +end + +% adding stripes into the signogram +sino_zing_rings = sino_zing; +coeff = linspace2(0.01,0.15,180); +vmax = max(sinogramIdeal(:)); +sino_zing_rings(1:180,120) = sino_zing_rings(1:180,120) + vmax*0.13; +sino_zing_rings(80:180,209) = sino_zing_rings(80:180,209) + vmax*0.14; +sino_zing_rings(50:110,210) = sino_zing_rings(50:110,210) + vmax*0.12; +sino_zing_rings(1:180,211) = sino_zing_rings(1:180,211) + vmax*0.14; +sino_zing_rings(1:180,300) = sino_zing_rings(1:180,300) + vmax*coeff(:); +sino_zing_rings(1:180,301) = sino_zing_rings(1:180,301) + vmax*0.14; +sino_zing_rings(10:100,302) = sino_zing_rings(10:100,302) + vmax*0.15; +sino_zing_rings(90:180,350) = sino_zing_rings(90:180,350) + vmax*0.11; +sino_zing_rings(60:140,410) = sino_zing_rings(60:140,410) + vmax*0.12; +sino_zing_rings(1:180,411) = sino_zing_rings(1:180,411) + vmax*0.14; +sino_zing_rings(1:180,412) = sino_zing_rings(1:180,412) + vmax*coeff(:); +sino_zing_rings(1:180,413) = sino_zing_rings(1:180,413) + vmax*coeff(:); +sino_zing_rings(1:180,500) = sino_zing_rings(1:180,500) - vmax*0.12; +sino_zing_rings(1:180,501) = sino_zing_rings(1:180,501) - vmax*0.12; +sino_zing_rings(1:180,550) = sino_zing_rings(1:180,550) + vmax*0.11; +sino_zing_rings(1:180,551) = sino_zing_rings(1:180,551) + vmax*0.11; +sino_zing_rings(1:180,552) = sino_zing_rings(1:180,552) + vmax*0.11; + +sino_zing_rings(sino_zing_rings < 0) = 0; +%% + +% adding Poisson noise +dose = 50000; +multifactor = 0.002; + +dataExp = dose.*exp(-sino_zing_rings*multifactor); % noiseless raw data +dataPnoise = astra_add_noise_to_sino(dataExp, dose); % pre-log noisy raw data (weights) +sino_zing_rings = log(dose./max(dataPnoise,1))/multifactor; %log corrected data -> sinogram +Dweights = dataPnoise'; % statistical weights +sino_zing_rings = sino_zing_rings'; +clear dataPnoise dataExp + +% w = dose./exp(sinogram*multifactor); % getting back raw data from log-cor + +% figure(1); +% set(gcf, 'Position', get(0,'Screensize')); +% subplot(1,2,1); imshow(phantom,[0 0.6]); title('Ideal Phantom'); colorbar; +% subplot(1,2,2); imshow(sinogram,[0 180]); title('Noisy Sinogram'); colorbar; +% colormap(cmapnew); + +% figure; +% set(gcf, 'Position', get(0,'Screensize')); +% subplot(1,2,1); imshow(sinogramIdeal,[0 180]); title('Ideal Sinogram'); colorbar; +% imshow(sino_zing_rings,[0 180]); title('Noisy Sinogram with zingers and stripes'); colorbar; +% colormap(cmapnew);
\ No newline at end of file |