diff options
Diffstat (limited to 'main_func')
20 files changed, 1479 insertions, 263 deletions
diff --git a/main_func/FISTA_REC.m b/main_func/FISTA_REC.m index 2823691..6987dca 100644 --- a/main_func/FISTA_REC.m +++ b/main_func/FISTA_REC.m @@ -1,24 +1,48 @@ function [X, output] = FISTA_REC(params) -% <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>> +% <<<< FISTA-based reconstruction routine using ASTRA-toolbox >>>> +% This code solves regularised PWLS problem using FISTA approach. +% The code contains multiple regularisation penalties as well as it can be +% accelerated by using ordered-subset version. Various projection +% geometries supported. + +% DISCLAIMER +% It is recommended to use ASTRA version 1.8 or later in order to avoid +% crashing due to GPU memory overflow for big datasets + % ___Input___: % params.[] file: +%----------------General Parameters------------------------ % - .proj_geom (geometry of the projector) [required] % - .vol_geom (geometry of the reconstructed object) [required] % - .sino (vectorized in 2D or 3D sinogram) [required] % - .iterFISTA (iterations for the main loop, default 40) % - .L_const (Lipschitz constant, default Power method) ) % - .X_ideal (ideal image, if given) -% - .weights (statisitcal weights, size of the sinogram) +% - .weights (statisitcal weights for the PWLS model, size of the sinogram) +% - .fidelity (use 'studentt' fidelity) % - .ROI (Region-of-interest, only if X_ideal is given) % - .initialize (a 'warm start' using SIRT method from ASTRA) %----------------Regularization choices------------------------ -% - .Regul_Lambda_FGPTV (FGP-TV regularization parameter) -% - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter) -% - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter) +% 1 .Regul_Lambda_FGPTV (FGP-TV regularization parameter) +% 2 .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter) +% 3 .Regul_LambdaLLT (Higher order LLT regularization parameter) +% 3.1 .Regul_tauLLT (time step parameter for LLT (HO) term) +% 4 .Regul_LambdaPatchBased_CPU (Patch-based nonlocal regularization parameter) +% 4.1 .Regul_PB_SearchW (ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window)) +% 4.2 .Regul_PB_SimilW (ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window)) +% 4.3 .Regul_PB_h (PB penalty function threshold) +% 5 .Regul_LambdaPatchBased_GPU (Patch-based nonlocal regularization parameter) +% 5.1 .Regul_PB_SearchW (ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window)) +% 5.2 .Regul_PB_SimilW (ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window)) +% 5.3 .Regul_PB_h (PB penalty function threshold) +% 6 .Regul_LambdaDiffHO (Higher-Order Diffusion regularization parameter) +% 6.1 .Regul_DiffHO_EdgePar (edge-preserving noise related parameter) +% 7 .Regul_LambdaTGV (Total Generalized variation regularization parameter) % - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04) % - .Regul_Iterations (iterations for the selected penalty, default 25) -% - .Regul_tauLLT (time step parameter for LLT term) +% - .Regul_Dimension ('2D' or '3D' way to apply regularization, '3D' is the default) +%----------------Ring removal------------------------ % - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal) % - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1) %----------------Visualization parameters------------------------ @@ -69,6 +93,15 @@ if (isfield(params,'weights')) else weights = ones(size(sino)); end +if (isfield(params,'fidelity')) + studentt = 0; + if (strcmp(params.fidelity,'studentt') == 1) + studentt = 1; + lambdaR_L1 = 0; + end +else + studentt = 0; +end if (isfield(params,'L_const')) L_const = params.L_const; else @@ -90,16 +123,16 @@ else for i = 1:niter [id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geomT, vol_geomT); s = norm(x1(:)); - x1 = x1/s; + x1 = x1./s; [sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT); y = sqweight.*y; astra_mex_data3d('delete', sino_id); astra_mex_data3d('delete', id); end - clear proj_geomT vol_geomT + %clear proj_geomT vol_geomT else % divergen beam geometry - fprintf('%s \n', 'Calculating Lipshitz constant for divergen beam geometry...'); + fprintf('%s \n', 'Calculating Lipshitz constant for divergen beam geometry... will take some time!'); niter = 8; % number of iteration for PM x1 = rand(N,N,SlicesZ); sqweight = sqrt(weights); @@ -150,8 +183,8 @@ if (isfield(params,'Regul_Iterations')) else IterationsRegul = 25; end -if (isfield(params,'Regul_LambdaHO')) - lambdaHO = params.Regul_LambdaHO; +if (isfield(params,'Regul_LambdaLLT')) + lambdaHO = params.Regul_LambdaLLT; else lambdaHO = 0; end @@ -165,6 +198,46 @@ if (isfield(params,'Regul_tauLLT')) else tauHO = 0.0001; end +if (isfield(params,'Regul_LambdaPatchBased_CPU')) + lambdaPB = params.Regul_LambdaPatchBased_CPU; +else + lambdaPB = 0; +end +if (isfield(params,'Regul_LambdaPatchBased_GPU')) + lambdaPB_GPU = params.Regul_LambdaPatchBased_GPU; +else + lambdaPB_GPU = 0; +end +if (isfield(params,'Regul_PB_SearchW')) + SearchW = params.Regul_PB_SearchW; +else + SearchW = 3; % default +end +if (isfield(params,'Regul_PB_SimilW')) + SimilW = params.Regul_PB_SimilW; +else + SimilW = 1; % default +end +if (isfield(params,'Regul_PB_h')) + h_PB = params.Regul_PB_h; +else + h_PB = 0.1; % default +end +if (isfield(params,'Regul_LambdaDiffHO')) + LambdaDiff_HO = params.Regul_LambdaDiffHO; +else + LambdaDiff_HO = 0; +end +if (isfield(params,'Regul_DiffHO_EdgePar')) + LambdaDiff_HO_EdgePar = params.Regul_DiffHO_EdgePar; +else + LambdaDiff_HO_EdgePar = 0.01; +end +if (isfield(params,'Regul_LambdaTGV')) + LambdaTGV = params.Regul_LambdaTGV; +else + LambdaTGV = 0; +end if (isfield(params,'Ring_LambdaR_L1')) lambdaR_L1 = params.Ring_LambdaR_L1; else @@ -175,6 +248,14 @@ if (isfield(params,'Ring_Alpha')) else alpha_ring = 1; end +if (isfield(params,'Regul_Dimension')) + Dimension = params.Regul_Dimension; + if ((strcmp('2D', Dimension) ~= 1) && (strcmp('3D', Dimension) ~= 1)) + Dimension = '3D'; + end +else + Dimension = '3D'; +end if (isfield(params,'show')) show = params.show; else @@ -216,94 +297,422 @@ if (isfield(params,'initialize')) else X = zeros(N,N,SlicesZ, 'single'); % storage for the solution end +if (isfield(params,'subsets')) + % Ordered Subsets reorganisation of data and angles + subsets = params.subsets; % subsets number + angles = proj_geom.ProjectionAngles; + binEdges = linspace(min(angles),max(angles),subsets+1); + + % assign values to bins + [binsDiscr,~] = histc(angles, [binEdges(1:end-1) Inf]); + + % get rearranged subset indices + IndicesReorg = zeros(length(angles),1); + counterM = 0; + for ii = 1:max(binsDiscr(:)) + counter = 0; + for jj = 1:subsets + curr_index = ii+jj-1 + counter; + if (binsDiscr(jj) >= ii) + counterM = counterM + 1; + IndicesReorg(counterM) = curr_index; + end + counter = (counter + binsDiscr(jj)) - 1; + end + end +else + subsets = 0; % Classical FISTA +end %----------------Reconstruction part------------------------ Resid_error = zeros(iterFISTA,1); % errors vector (if the ground truth is given) objective = zeros(iterFISTA,1); % objective function values vector -t = 1; -X_t = X; -r = zeros(Detectors,SlicesZ, 'single'); % 2D array (for 3D data) of sparse "ring" vectors -r_x = r; % another ring variable -residual = zeros(size(sino),'single'); - -% Outer FISTA iterations loop -for i = 1:iterFISTA - - X_old = X; - t_old = t; - r_old = r; +if (subsets == 0) + % Classical FISTA + t = 1; + X_t = X; - [sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom); + r = zeros(Detectors,SlicesZ, 'single'); % 2D array (for 3D data) of sparse "ring" vectors + r_x = r; % another ring variable + residual = zeros(size(sino),'single'); - if (lambdaR_L1 > 0) - % ring removal part (Group-Huber fidelity) - for kkk = 1:anglesNumb - residual(:,kkk,:) = squeeze(weights(:,kkk,:)).*(squeeze(sino_updt(:,kkk,:)) - (squeeze(sino(:,kkk,:)) - alpha_ring.*r_x)); - end - vec = sum(residual,2); - if (SlicesZ > 1) - vec = squeeze(vec(:,1,:)); + % Outer FISTA iterations loop + for i = 1:iterFISTA + + X_old = X; + t_old = t; + r_old = r; + + % if the geometry is parallel use slice-by-slice projection-backprojection routine + if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'parallel3d')) + sino_updt = zeros(size(sino),'single'); + for kkk = 1:SlicesZ + [sino_id, sino_updt(:,:,kkk)] = astra_create_sino3d_cuda(X_t(:,:,kkk), proj_geomT, vol_geomT); + astra_mex_data3d('delete', sino_id); + end + else + % for divergent 3D geometry (watch the GPU memory overflow in earlier ASTRA versions < 1.8) + [sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom); + end + + if (lambdaR_L1 > 0) + % the ring removal part (Group-Huber fidelity) + for kkk = 1:anglesNumb + residual(:,kkk,:) = squeeze(weights(:,kkk,:)).*(squeeze(sino_updt(:,kkk,:)) - (squeeze(sino(:,kkk,:)) - alpha_ring.*r_x)); + end + vec = sum(residual,2); + if (SlicesZ > 1) + vec = squeeze(vec(:,1,:)); + end + r = r_x - (1./L_const).*vec; + objective(i) = (0.5*sum(residual(:).^2)); % for the objective function output + else + if (studentt == 1) + % artifacts removal with Students t penalty + residual = weights.*(sino_updt - sino); + for kkk = 1:SlicesZ + res_vec = reshape(residual(:,:,kkk), Detectors*anglesNumb, 1); % 1D vectorized sinogram + %s = 100; + %gr = (2)*res_vec./(s*2 + conj(res_vec).*res_vec); + [ff, gr] = studentst(res_vec, 1); + residual(:,:,kkk) = reshape(gr, Detectors, anglesNumb); + end + objective(i) = ff; % for the objective function output + else + % no ring removal (LS model) + residual = weights.*(sino_updt - sino); + objective(i) = (0.5*sum(residual(:).^2)); % for the objective function output + end + end + + % if the geometry is parallel use slice-by-slice projection-backprojection routine + if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'parallel3d')) + x_temp = zeros(size(X),'single'); + for kkk = 1:SlicesZ + [id, x_temp(:,:,kkk)] = astra_create_backprojection3d_cuda(squeeze(residual(:,:,kkk)), proj_geomT, vol_geomT); + astra_mex_data3d('delete', id); + end + else + [id, x_temp] = astra_create_backprojection3d_cuda(residual, proj_geom, vol_geom); + end + X = X_t - (1/L_const).*x_temp; + astra_mex_data3d('delete', sino_id); + astra_mex_data3d('delete', id); + + % regularization + if (lambdaFGP_TV > 0) + % FGP-TV regularization + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + [X(:,:,kkk), f_val] = FGP_TV(single(X(:,:,kkk)), lambdaFGP_TV, IterationsRegul, tol, 'iso'); + end + else + % 3D regularization + [X, f_val] = FGP_TV(single(X), lambdaFGP_TV, IterationsRegul, tol, 'iso'); + end + objective(i) = (objective(i) + f_val)./(Detectors*anglesNumb*SlicesZ); + end + if (lambdaSB_TV > 0) + % Split Bregman regularization + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + X(:,:,kkk) = SplitBregman_TV(single(X(:,:,kkk)), lambdaSB_TV, IterationsRegul, tol); % (more memory efficent) + end + else + % 3D regularization + X = SplitBregman_TV(single(X), lambdaSB_TV, IterationsRegul, tol); % (more memory efficent) + end + end + if (lambdaHO > 0) + % Higher Order (LLT) regularization + X2 = zeros(N,N,SlicesZ,'single'); + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + X2(:,:,kkk) = LLT_model(single(X(:,:,kkk)), lambdaHO, tauHO, iterHO, 3.0e-05, 0); + end + else + % 3D regularization + X2 = LLT_model(single(X), lambdaHO, tauHO, iterHO, 3.0e-05, 0); + end + X = 0.5.*(X + X2); % averaged combination of two solutions + + end + if (lambdaPB > 0) + % Patch-Based regularization (can be very slow on CPU) + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + X(:,:,kkk) = PatchBased_Regul(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB); + end + else + X = PatchBased_Regul(single(X), SearchW, SimilW, h_PB, lambdaPB); + end + end + if (lambdaPB_GPU > 0) + % Patch-Based regularization (GPU CUDA implementation) + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + X(:,:,kkk) = NLM_GPU(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB_GPU); + end + else + X = NLM_GPU(single(X), SearchW, SimilW, h_PB, lambdaPB_GPU); + end + end + if (LambdaDiff_HO > 0) + % Higher-order diffusion penalty (GPU CUDA implementation) + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + X(:,:,kkk) = Diff4thHajiaboli_GPU(single(X(:,:,kkk)), LambdaDiff_HO_EdgePar, LambdaDiff_HO, IterationsRegul); + end + else + X = Diff4thHajiaboli_GPU(X, LambdaDiff_HO_EdgePar, LambdaDiff_HO, IterationsRegul); + end + end + if (LambdaTGV > 0) + % Total Generalized variation (currently only 2D) + lamTGV1 = 1.1; % smoothing trade-off parameters, see Pock's paper + lamTGV2 = 0.8; % second-order term + for kkk = 1:SlicesZ + X(:,:,kkk) = TGV_PD(single(X(:,:,kkk)), LambdaTGV, lamTGV1, lamTGV2, IterationsRegul); + end + end + + if (lambdaR_L1 > 0) + r = max(abs(r)-lambdaR_L1, 0).*sign(r); % soft-thresholding operator for ring vector + end + + t = (1 + sqrt(1 + 4*t^2))/2; % updating t + X_t = X + ((t_old-1)/t).*(X - X_old); % updating X + + if (lambdaR_L1 > 0) + r_x = r + ((t_old-1)/t).*(r - r_old); % updating r + end + + if (show == 1) + figure(10); imshow(X(:,:,slice), [0 maxvalplot]); + if (lambdaR_L1 > 0) + figure(11); plot(r); title('Rings offset vector') + end + pause(0.01); + end + if (strcmp(X_ideal, 'none' ) == 0) + Resid_error(i) = RMSE(X(ROI), X_ideal(ROI)); + fprintf('%s %i %s %s %.4f %s %s %f \n', 'Iteration Number:', i, '|', 'Error RMSE:', Resid_error(i), '|', 'Objective:', objective(i)); + else + fprintf('%s %i %s %s %f \n', 'Iteration Number:', i, '|', 'Objective:', objective(i)); end - r = r_x - (1./L_const).*vec; - else - % no ring removal - residual = weights.*(sino_updt - sino); - end - - objective(i) = (0.5*norm(residual(:))^2)/(Detectors*anglesNumb*SlicesZ); % for the objective function output - - [id, x_temp] = astra_create_backprojection3d_cuda(residual, proj_geom, vol_geom); - - X = X_t - (1/L_const).*x_temp; - astra_mex_data3d('delete', sino_id); - astra_mex_data3d('delete', id); - - if (lambdaFGP_TV > 0) - % FGP-TV regularization - [X, f_val] = FGP_TV(single(X), lambdaFGP_TV, IterationsRegul, tol, 'iso'); - objective(i) = objective(i) + f_val; - end - if (lambdaSB_TV > 0) - % Split Bregman regularization - X = SplitBregman_TV(single(X), lambdaSB_TV, IterationsRegul, tol); % (more memory efficent) - end - if (lambdaHO > 0) - % Higher Order (LLT) regularization - X2 = LLT_model(single(X), lambdaHO, tauHO, iterHO, 3.0e-05, 0); - X = 0.5.*(X + X2); % averaged combination of two solutions - end - - - - if (lambdaR_L1 > 0) - r = max(abs(r)-lambdaR_L1, 0).*sign(r); % soft-thresholding operator for ring vector end +else + % Ordered Subsets (OS) FISTA reconstruction routine (normally one order of magnitude faster than classical) + t = 1; + X_t = X; + proj_geomSUB = proj_geom; - t = (1 + sqrt(1 + 4*t^2))/2; % updating t - X_t = X + ((t_old-1)/t).*(X - X_old); % updating X - if (lambdaR_L1 > 0) - r_x = r + ((t_old-1)/t).*(r - r_old); % updating r - end + r = zeros(Detectors,SlicesZ, 'single'); % 2D array (for 3D data) of sparse "ring" vectors + r_x = r; % another ring variable + residual2 = zeros(size(sino),'single'); - if (show == 1) - figure(10); imshow(X(:,:,slice), [0 maxvalplot]); - if (lambdaR_L1 > 0) - figure(11); plot(r); title('Rings offset vector') + % Outer FISTA iterations loop + for i = 1:iterFISTA + + % With OS approach it becomes trickier to correlate independent subsets, hence additional work is required + % one solution is to work with a full sinogram at times + if ((i >= 3) && (lambdaR_L1 > 0)) + [sino_id2, sino_updt2] = astra_create_sino3d_cuda(X, proj_geom, vol_geom); + astra_mex_data3d('delete', sino_id2); + end + + % subsets loop + counterInd = 1; + for ss = 1:subsets + X_old = X; + t_old = t; + r_old = r; + + numProjSub = binsDiscr(ss); % the number of projections per subset + CurrSubIndeces = IndicesReorg(counterInd:(counterInd + numProjSub - 1)); % extract indeces attached to the subset + proj_geomSUB.ProjectionAngles = angles(CurrSubIndeces); + + if (lambdaR_L1 > 0) + + % the ring removal part (Group-Huber fidelity) + % first 2 iterations do additional work reconstructing whole dataset to ensure + % the stablility + if (i < 3) + [sino_id2, sino_updt2] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom); + astra_mex_data3d('delete', sino_id2); + else + [sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geomSUB, vol_geom); + end + + for kkk = 1:anglesNumb + residual2(:,kkk,:) = squeeze(weights(:,kkk,:)).*(squeeze(sino_updt2(:,kkk,:)) - (squeeze(sino(:,kkk,:)) - alpha_ring.*r_x)); + end + + residual = zeros(Detectors, numProjSub, SlicesZ,'single'); + for kkk = 1:numProjSub + indC = CurrSubIndeces(kkk); + if (i < 3) + residual(:,kkk,:) = squeeze(residual2(:,indC,:)); + else + residual(:,kkk,:) = squeeze(weights(:,indC,:)).*(squeeze(sino_updt(:,kkk,:)) - (squeeze(sino(:,indC,:)) - alpha_ring.*r_x)); + end + end + vec = sum(residual2,2); + if (SlicesZ > 1) + vec = squeeze(vec(:,1,:)); + end + r = r_x - (1./L_const).*vec; + else + [sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geomSUB, vol_geom); + + if (studentt == 1) + % artifacts removal with Students t penalty + residual = squeeze(weights(:,CurrSubIndeces,:)).*(sino_updt - squeeze(sino(:,CurrSubIndeces,:))); + + for kkk = 1:SlicesZ + res_vec = reshape(residual(:,:,kkk), Detectors*numProjSub, 1); % 1D vectorized sinogram + %s = 100; + %gr = (2)*res_vec./(s*2 + conj(res_vec).*res_vec); + [ff, gr] = studentst(res_vec, 1); + residual(:,:,kkk) = reshape(gr, Detectors, numProjSub); + end + objective(i) = ff; % for the objective function output + else + % no ring removal (LS model) + residual = squeeze(weights(:,CurrSubIndeces,:)).*(sino_updt - squeeze(sino(:,CurrSubIndeces,:))); + end + end + + [id, x_temp] = astra_create_backprojection3d_cuda(residual, proj_geomSUB, vol_geom); + + X = X_t - (1/L_const).*x_temp; + astra_mex_data3d('delete', sino_id); + astra_mex_data3d('delete', id); + + % regularization + if (lambdaFGP_TV > 0) + % FGP-TV regularization + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + [X(:,:,kkk), f_val] = FGP_TV(single(X(:,:,kkk)), lambdaFGP_TV/subsets, IterationsRegul, tol, 'iso'); + end + else + % 3D regularization + [X, f_val] = FGP_TV(single(X), lambdaFGP_TV/subsets, IterationsRegul, tol, 'iso'); + end + objective(i) = objective(i) + f_val; + end + if (lambdaSB_TV > 0) + % Split Bregman regularization + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + X(:,:,kkk) = SplitBregman_TV(single(X(:,:,kkk)), lambdaSB_TV/subsets, IterationsRegul, tol); % (more memory efficent) + end + else + % 3D regularization + X = SplitBregman_TV(single(X), lambdaSB_TV/subsets, IterationsRegul, tol); % (more memory efficent) + end + end + if (lambdaHO > 0) + % Higher Order (LLT) regularization + X2 = zeros(N,N,SlicesZ,'single'); + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + X2(:,:,kkk) = LLT_model(single(X(:,:,kkk)), lambdaHO/subsets, tauHO/subsets, iterHO, 2.0e-05, 0); + end + else + % 3D regularization + X2 = LLT_model(single(X), lambdaHO/subsets, tauHO/subsets, iterHO, 2.0e-05, 0); + end + X = 0.5.*(X + X2); % the averaged combination of two solutions + end + if (lambdaPB > 0) + % Patch-Based regularization (can be slow on CPU) + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + X(:,:,kkk) = PatchBased_Regul(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB/subsets); + end + else + X = PatchBased_Regul(single(X), SearchW, SimilW, h_PB, lambdaPB/subsets); + end + end + if (lambdaPB_GPU > 0) + % Patch-Based regularization (GPU CUDA implementation) + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + X(:,:,kkk) = NLM_GPU(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB_GPU); + end + else + X = NLM_GPU(single(X), SearchW, SimilW, h_PB, lambdaPB_GPU); + end + end + if (LambdaDiff_HO > 0) + % Higher-order diffusion penalty (GPU CUDA implementation) + if ((strcmp('2D', Dimension) == 1)) + % 2D regularization + for kkk = 1:SlicesZ + X(:,:,kkk) = Diff4thHajiaboli_GPU(single(X(:,:,kkk)), LambdaDiff_HO_EdgePar, LambdaDiff_HO, round(IterationsRegul/subsets)); + end + else + X = Diff4thHajiaboli_GPU(X, LambdaDiff_HO_EdgePar, LambdaDiff_HO, round(IterationsRegul/subsets)); + end + end + if (LambdaTGV > 0) + % Total Generalized variation (currently only 2D) + lamTGV1 = 1.1; % smoothing trade-off parameters, see Pock's paper + lamTGV2 = 0.5; % second-order term + for kkk = 1:SlicesZ + X(:,:,kkk) = TGV_PD(single(X(:,:,kkk)), LambdaTGV/subsets, lamTGV1, lamTGV2, IterationsRegul); + end + end + + if (lambdaR_L1 > 0) + r = max(abs(r)-lambdaR_L1, 0).*sign(r); % soft-thresholding operator for ring vector + end + + t = (1 + sqrt(1 + 4*t^2))/2; % updating t + X_t = X + ((t_old-1)/t).*(X - X_old); % updating X + + if (lambdaR_L1 > 0) + r_x = r + ((t_old-1)/t).*(r - r_old); % updating r + end + + counterInd = counterInd + numProjSub; + end + + if (show == 1) + figure(10); imshow(X(:,:,slice), [0 maxvalplot]); + if (lambdaR_L1 > 0) + figure(11); plot(r); title('Rings offset vector') + end + pause(0.01); + end + + if (strcmp(X_ideal, 'none' ) == 0) + Resid_error(i) = RMSE(X(ROI), X_ideal(ROI)); + fprintf('%s %i %s %s %.4f %s %s %f \n', 'Iteration Number:', i, '|', 'Error RMSE:', Resid_error(i), '|', 'Objective:', objective(i)); + else + fprintf('%s %i %s %s %f \n', 'Iteration Number:', i, '|', 'Objective:', objective(i)); end - pause(0.01); - end - if (strcmp(X_ideal, 'none' ) == 0) - Resid_error(i) = RMSE(X(ROI), X_ideal(ROI)); - fprintf('%s %i %s %s %.4f %s %s %f \n', 'Iteration Number:', i, '|', 'Error RMSE:', Resid_error(i), '|', 'Objective:', objective(i)); - else - fprintf('%s %i %s %s %f \n', 'Iteration Number:', i, '|', 'Objective:', objective(i)); end end + output.Resid_error = Resid_error; output.objective = objective; output.L_const = L_const; -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + end diff --git a/main_func/compile_mex.m b/main_func/compile_mex.m index 7bfa8eb..66c05da 100644 --- a/main_func/compile_mex.m +++ b/main_func/compile_mex.m @@ -1,10 +1,11 @@ -% compile mex's once +% compile mex's in Matlab once cd regularizers_CPU/ -mex LLT_model.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" -mex FGP_TV.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" -mex SplitBregman_TV.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" -mex TGV_PD.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +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/main_func/regularizers_CPU/FGP_TV.c b/main_func/regularizers_CPU/FGP_TV.c index b9dc57f..66442c9 100644 --- a/main_func/regularizers_CPU/FGP_TV.c +++ b/main_func/regularizers_CPU/FGP_TV.c @@ -55,7 +55,7 @@ void mexFunction( { 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, funcval; + 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]); @@ -79,7 +79,6 @@ void mexFunction( mxFree(penalty_type); } /*output function value (last iteration) */ - funcval = 0.0f; plhs[1] = mxCreateNumericMatrix(1, 1, mxSINGLE_CLASS, mxREAL); float *funcvalA = (float *) mxGetData(plhs[1]); @@ -118,7 +117,7 @@ void mexFunction( /*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); + Rupd_func2D(P1, P1_old, P2, P2_old, R1, R2, tkp1, tk, dimX, dimY); /* calculate norm */ re = 0.0f; re1 = 0.0f; @@ -130,23 +129,17 @@ void mexFunction( re = sqrt(re)/sqrt(re1); if (re < epsil) count++; if (count > 3) { - Obj_func2D(A, D, P1, P2, lambda, dimX, dimY); - funcval = 0.0f; - for(j=0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j],2); - funcvalA[0] = sqrt(funcval); + 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_func2D(A, D, P1, P2, lambda, dimX, dimY); - funcval = 0.0f; - for(j=0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j],2); - funcvalA[0] = sqrt(funcval); + Obj_func_CALC2D(A, D, funcvalA, lambda, dimX, dimY); break; }} re_old = re; - /*printf("%f %i %i \n", re, ll, count); */ - + /*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); @@ -154,12 +147,7 @@ void mexFunction( tk = tkp1; /* calculating the objective function value */ - if (ll == (iter-1)) { - Obj_func2D(A, D, P1, P2, lambda, dimX, dimY); - funcval = 0.0f; - for(j=0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j],2); - funcvalA[0] = sqrt(funcval); - } + 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]); } @@ -203,21 +191,14 @@ void mexFunction( /* stop if the norm residual is less than the tolerance EPS */ if (re < epsil) count++; if (count > 3) { - Obj_func3D(A, D, P1, P2, P3,lambda, dimX, dimY, dimZ); - funcval = 0.0f; - for(j=0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j],2); - funcvalA[0] = sqrt(funcval); + 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_func3D(A, D, P1, P2, P3,lambda, dimX, dimY, dimZ); - funcval = 0.0f; - for(j=0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j],2); - funcvalA[0] = sqrt(funcval); - break; }} - + Obj_func_CALC3D(A, D, funcvalA, lambda, dimX, dimY, dimZ); + }} re_old = re; /*printf("%f %i %i \n", re, ll, count); */ @@ -228,13 +209,7 @@ void mexFunction( copyIm(P3, P3_old, dimX, dimY, dimZ); tk = tkp1; - if (ll == (iter-1)) { - Obj_func3D(A, D, P1, P2, P3,lambda, dimX, dimY, dimZ); - funcval = 0.0f; - for(j=0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j],2); - funcvalA[0] = sqrt(funcval); - } - + 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/main_func/regularizers_CPU/FGP_TV_core.c b/main_func/regularizers_CPU/FGP_TV_core.c index ed5d337..03cd445 100644 --- a/main_func/regularizers_CPU/FGP_TV_core.c +++ b/main_func/regularizers_CPU/FGP_TV_core.c @@ -19,8 +19,58 @@ 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; @@ -105,6 +155,31 @@ float Rupd_func2D(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, /* 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; diff --git a/main_func/regularizers_CPU/FGP_TV_core.h b/main_func/regularizers_CPU/FGP_TV_core.h index 778d489..6430bf2 100644 --- a/main_func/regularizers_CPU/FGP_TV_core.h +++ b/main_func/regularizers_CPU/FGP_TV_core.h @@ -59,11 +59,13 @@ float Obj_func2D(float *A, float *D, float *R1, float *R2, float lambda, int dim 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/main_func/regularizers_CPU/LLT_model.c b/main_func/regularizers_CPU/LLT_model.c index 19e0109..0b07b47 100644 --- a/main_func/regularizers_CPU/LLT_model.c +++ b/main_func/regularizers_CPU/LLT_model.c @@ -20,6 +20,7 @@ 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: diff --git a/main_func/regularizers_CPU/LLT_model_core.c b/main_func/regularizers_CPU/LLT_model_core.c index e027231..3a853d2 100644 --- a/main_func/regularizers_CPU/LLT_model_core.c +++ b/main_func/regularizers_CPU/LLT_model_core.c @@ -19,6 +19,31 @@ 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; diff --git a/main_func/regularizers_CPU/LLT_model_core.h b/main_func/regularizers_CPU/LLT_model_core.h index 273c89b..13fce5a 100644 --- a/main_func/regularizers_CPU/LLT_model_core.h +++ b/main_func/regularizers_CPU/LLT_model_core.h @@ -27,31 +27,6 @@ limitations under the License. #define EPS 0.01 -/* 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); -* -* -* 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 -*/ /* 2D functions */ #ifdef __cplusplus extern "C" { diff --git a/main_func/regularizers_CPU/PatchBased_Regul.c b/main_func/regularizers_CPU/PatchBased_Regul.c index e5748dc..9c925df 100644 --- a/main_func/regularizers_CPU/PatchBased_Regul.c +++ b/main_func/regularizers_CPU/PatchBased_Regul.c @@ -28,27 +28,23 @@ limitations under the License. * 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
+ * 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)
*
- * Quick 2D denoising example in Matlab:
+ * 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
-
+ ImDen = PatchBased_Regul(single(u0), 3, 1, 0.08, 0.05);
*
* Matlab + C/mex compilers needed
- * to compile with OMP support: mex PB_Regul_CPU.c CFLAGS="\$CFLAGS -fopenmp -Wall" LDFLAGS="\$LDFLAGS -fopenmp"
+ * to compile with OMP support: mex PatchBased_Regul.c CFLAGS="\$CFLAGS -fopenmp -Wall" LDFLAGS="\$LDFLAGS -fopenmp"
*
* D. Kazantsev *
* 02/07/2014
@@ -71,17 +67,23 @@ void mexFunction( M = dims[1];
Z = dims[2];
- if ((numdims < 2) || (numdims > 3)) {mexErrMsgTxt("The input should be 2D image or 3D volume");}
+ 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 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]); /* regularization parameter */
+ 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");
@@ -90,7 +92,6 @@ void mexFunction( /* 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 */
@@ -136,4 +137,4 @@ void mexFunction( switchpad_crop = 1; /*cropping*/
pad_crop(Bp, B, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
} /*end else ndims*/
-}
\ No newline at end of file +}
diff --git a/main_func/regularizers_CPU/PatchBased_Regul_core.c b/main_func/regularizers_CPU/PatchBased_Regul_core.c index 6f0a48d..acfb464 100644 --- a/main_func/regularizers_CPU/PatchBased_Regul_core.c +++ b/main_func/regularizers_CPU/PatchBased_Regul_core.c @@ -19,40 +19,33 @@ 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 (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 +/* 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) -* -* 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 -*/ + * 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*/ +/*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; diff --git a/main_func/regularizers_CPU/SplitBregman_TV_core.c b/main_func/regularizers_CPU/SplitBregman_TV_core.c index ce9ef93..4109a4b 100644 --- a/main_func/regularizers_CPU/SplitBregman_TV_core.c +++ b/main_func/regularizers_CPU/SplitBregman_TV_core.c @@ -37,7 +37,6 @@ limitations under the License. * 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* diff --git a/main_func/regularizers_CPU/TGV_PD.c b/main_func/regularizers_CPU/TGV_PD.c index 6a7697b..c9cb440 100644 --- a/main_func/regularizers_CPU/TGV_PD.c +++ b/main_func/regularizers_CPU/TGV_PD.c @@ -37,9 +37,9 @@ limitations under the License. * 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; + * tic; u = TGV_PD(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" + * 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" * @@ -53,7 +53,7 @@ void mexFunction( { int number_of_dims, iter, dimX, dimY, dimZ, ll; const int *dim_array; - float *A, *U, *U_old, *P1, *P2, *P3, *Q1, *Q2, *Q3, *Q4, *Q5, *Q6, *Q7, *Q8, *Q9, *V1, *V1_old, *V2, *V2_old, *V3, *V3_old, lambda, L2, tau, sigma, alpha1, alpha0; + 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]); @@ -88,48 +88,17 @@ void mexFunction( 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)); } - else if (number_of_dims == 3) { - mexErrMsgTxt("The input data should be 2D"); - /*3D case*/ -// dimZ = dim_array[2]; -// U = (float*)mxGetPr(plhs[0] = 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)); -// -// Q1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// Q2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// Q3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// Q4 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// Q5 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// Q6 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// Q7 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// Q8 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// Q9 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// -// U_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// -// V1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// V1_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// V2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// V2_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// V3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); -// V3_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); - } - else {mexErrMsgTxt("The input data should be 2D");} - - - /*printf("%i \n", i);*/ - L2 = 12.0; /*Lipshitz constant*/ + 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); - if (number_of_dims == 2) { /* Here primal-dual iterations begin for 2D */ for(ll = 0; ll < iter; ll++) { @@ -164,23 +133,12 @@ void mexFunction( /*get new V*/ newU(V1, V1_old, dimX, dimY, dimZ); newU(V2, V2_old, dimX, dimY, dimZ); - } /*end of iterations*/ + } /*end of iterations*/ } - -// /*3D version*/ -// if (number_of_dims == 3) { -// /* Here primal-dual iterations begin for 3D */ -// for(ll = 0; ll < iter; ll++) { -// -// /* Calculate Dual Variable P */ -// DualP_3D(U, V1, V2, V3, P1, P2, P3, dimX, dimY, dimZ, sigma); -// -// /*Projection onto convex set for P*/ -// ProjP_3D(P1, P2, P3, dimX, dimY, dimZ, alpha1); -// -// /* Calculate Dual Variable Q */ -// DualQ_3D(V1, V2, V2, Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9, dimX, dimY, dimZ, sigma); -// -// } /*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/main_func/regularizers_CPU/TGV_PD_core.c b/main_func/regularizers_CPU/TGV_PD_core.c index ec7cadb..4139d10 100644 --- a/main_func/regularizers_CPU/TGV_PD_core.c +++ b/main_func/regularizers_CPU/TGV_PD_core.c @@ -38,7 +38,6 @@ limitations under the License. * 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" * diff --git a/main_func/regularizers_CPU/TGV_PD_core.h b/main_func/regularizers_CPU/TGV_PD_core.h index 25a8216..d5378df 100644 --- a/main_func/regularizers_CPU/TGV_PD_core.h +++ b/main_func/regularizers_CPU/TGV_PD_core.h @@ -60,9 +60,6 @@ float DualQ_2D(float *V1, float *V2, float *Q1, float *Q2, float *Q3, int dimX, 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); -/*3D functions*/ -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); - 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 diff --git a/main_func/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp b/main_func/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp new file mode 100644 index 0000000..5a8c7c0 --- /dev/null +++ b/main_func/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/main_func/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.cu b/main_func/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.cu new file mode 100644 index 0000000..178af00 --- /dev/null +++ b/main_func/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/main_func/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.h b/main_func/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.h new file mode 100644 index 0000000..cfbb45a --- /dev/null +++ b/main_func/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/main_func/regularizers_GPU/NL_Regul/NLM_GPU.cpp b/main_func/regularizers_GPU/NL_Regul/NLM_GPU.cpp new file mode 100644 index 0000000..ff0cc90 --- /dev/null +++ b/main_func/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/main_func/regularizers_GPU/NL_Regul/NLM_GPU_kernel.cu b/main_func/regularizers_GPU/NL_Regul/NLM_GPU_kernel.cu new file mode 100644 index 0000000..17da3a8 --- /dev/null +++ b/main_func/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/main_func/regularizers_GPU/NL_Regul/NLM_GPU_kernel.h b/main_func/regularizers_GPU/NL_Regul/NLM_GPU_kernel.h new file mode 100644 index 0000000..bc9d4a3 --- /dev/null +++ b/main_func/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 |