Major reorganization, updated routines

4 files changed, 0 insertions, 545 deletions

diff --git a/demo/Demo1.m b/demo/Demo1.m
deleted file mode 100644
index 08d46e1..0000000
--- a/demo/Demo1.m
+++ /dev/null
@@ -1,160 +0,0 @@
-% Demonstration of tomographic reconstruction from noisy and corrupted by 
-% artifacts undersampled projection data using Students't penalty 
-% Optimisation problem is solved using FISTA algorithm (see Beck & Teboulle)
-
-% see ReadMe file for instructions
-clear all
-close all 
-
-% adding paths
-addpath('data/'); 
-addpath('main_func/'); 
-addpath('supp/'); 
-
-load phantom_bone512.mat % load the phantom
-load  my_red_yellowMAP.mat % load the colormap
-% load sino1.mat; % load noisy sinogram
-
-N = 512; %  the size of the tomographic image NxN
-theta = 1:1:180; % acquisition angles (in parallel beam from 0 to Pi)
-theta_rad = theta*(pi/180); % conversion to radians
-P = 2*ceil(N/sqrt(2))+1; % the size of the detector array
-ROI = find(phantom > 0);
-
-zing_rings_add;    % generating data, adding zingers and stripes
-
-%% 
-fprintf('%s\n', 'Direct reconstruction using FBP...');
-FBP_1 = iradon(sino_zing_rings', theta, N); 
-
-fprintf('%s %.4f\n', 'RMSE for FBP reconstruction:', RMSE(FBP_1(:), phantom(:)));
-
-figure(1); 
-subplot_tight(1,2,1, [0.05 0.05]); imshow(FBP_1,[0 0.6]);  title('FBP reconstruction of noisy and corrupted by artifacts sinogram'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); imshow((phantom - FBP_1).^2,[0 0.1]);  title('residual: (ideal phantom - FBP)^2'); colorbar;
-colormap(cmapnew); 
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-LS without regularization...');
-clear params
-% define parameters
-params.sino = sino_zing_rings;
-params.N = N; % image size 
-params.angles = theta_rad; % angles in radians 
-params.iterFISTA = 180; %max number of outer iterations
-params.X_ideal = phantom; % ideal phantom
-params.ROI = ROI; % phantom region-of-interest
-params.show = 0; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-params.weights = Dweights; % statistical weighting 
-tic; [X_FISTA, error_FISTA, obj_FISTA, sinoFISTA] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-LS reconstruction is:', min(error_FISTA(:)));
-
-figure(2); clf
-%set(gcf, 'Position', get(0,'Screensize'));
-subplot_tight(1,2,1, [0.05 0.05]); imshow(X_FISTA,[0 0.6]); title('FISTA-LS reconstruction'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); imshow((phantom - X_FISTA).^2,[0 0.1]);  title('residual'); colorbar;
-colormap(cmapnew); 
-figure(3); clf
-subplot_tight(1,2,1, [0.05 0.05]); plot(error_FISTA);  title('RMSE plot'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); plot(obj_FISTA);  title('Objective plot'); colorbar;
-colormap(cmapnew); 
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-LS-TV...');
-clear params
-% define parameters
-params.sino = sino_zing_rings;
-params.N = N; % image size 
-params.angles = theta_rad; % angles in radians 
-params.iterFISTA = 200; % max number of outer iterations
-params.lambdaTV = 5.39e-05; % regularization parameter for TV problem
-params.tol = 1.0e-04; % tolerance to terminate TV iterations
-params.iterTV = 20; % the max number of TV iterations
-params.X_ideal = phantom; % ideal phantom
-params.ROI = ROI; % phantom region-of-interest
-params.weights = Dweights; % statistical weighting 
-params.show = 0; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-tic; [X_FISTA_TV, error_FISTA_TV, obj_FISTA_TV, sinoFISTA_TV] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-LS-TV reconstruction is:', min(error_FISTA_TV(:)));
-
-figure(4); clf
-subplot_tight(1,2,1, [0.05 0.05]); imshow(X_FISTA_TV,[0 0.6]); title('FISTA-LS-TV reconstruction'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); imshow((phantom - X_FISTA_TV).^2,[0 0.1]);  title('residual'); colorbar;
-colormap(cmapnew); 
-figure(5); clf
-subplot_tight(1,2,1, [0.05 0.05]); plot(error_FISTA_TV);  title('RMSE plot'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); plot(obj_FISTA_TV);  title('Objective plot'); colorbar;
-colormap(cmapnew); 
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-GH-TV...');
-clear params
-% define parameters
-params.sino = sino_zing_rings;
-params.N = N; % image size 
-params.angles = theta_rad; % angles in radians 
-params.iterFISTA = 60; % max number of outer iterations
-params.lambdaTV = 0.002526;  % regularization parameter for TV problem
-params.tol = 1.0e-04; % tolerance to terminate TV iterations
-params.iterTV = 20; % the max number of TV iterations
-params.X_ideal = phantom; % ideal phantom
-params.ROI = ROI; % phantom region-of-interest
-params.weights = Dweights; % statistical weighting 
-params.lambdaR_L1 = 0.002; % parameter to sparsify the "rings vector"
-params.show = 0; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-tic; [X_FISTA_GH_TV, error_FISTA_GH_TV, obj_FISTA_GH_TV, sinoFISTA_GH_TV] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-GH-TV reconstruction is:', min(error_FISTA_GH_TV(:)));
-
-figure(6); clf
-subplot_tight(1,2,1, [0.05 0.05]); imshow(X_FISTA_GH_TV,[0 0.6]); title('FISTA-GH-TV reconstruction'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]);imshow((phantom - X_FISTA_GH_TV).^2,[0 0.1]);  title('residual'); colorbar;
-colormap(cmapnew); 
-
-figure(7); clf
-subplot_tight(1,2,1, [0.05 0.05]);  plot(error_FISTA_GH_TV);  title('RMSE plot'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]);  plot(obj_FISTA_GH_TV);  title('Objective plot'); colorbar;
-colormap(cmapnew); 
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-Student-TV...');
-clear params
-% define parameters
-params.sino = sino_zing_rings;
-params.N = N; % image size 
-params.angles = theta_rad; % angles in radians 
-params.iterFISTA = 67; % max number of outer iterations
-%params.L_const = 80000; % Lipshitz constant (can be chosen manually to accelerate convergence)
-params.lambdaTV = 0.00152;  % regularization parameter for TV problem
-params.tol = 1.0e-04; % tolerance to terminate TV iterations
-params.iterTV = 20; % the max number of TV iterations
-params.X_ideal = phantom; % ideal phantom
-params.ROI = ROI; % phantom region-of-interest
-params.weights = Dweights; % statistical weighting 
-params.fidelity = 'student'; % selecting students t fidelity
-params.show = 0; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-tic; [X_FISTA_student_TV, error_FISTA_student_TV, obj_FISTA_student_TV, sinoFISTA_student_TV] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-Student-TV reconstruction is:', min(error_FISTA_student_TV(:)));
-
-figure(8); 
-set(gcf, 'Position', get(0,'Screensize'));
-subplot_tight(1,2,1, [0.05 0.05]); imshow(X_FISTA_student_TV,[0 0.6]); title('FISTA-Student-TV reconstruction'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); imshow((phantom - X_FISTA_student_TV).^2,[0 0.1]);  title('residual'); colorbar;
-colormap(cmapnew); 
-
-figure(9); 
-subplot_tight(1,2,1, [0.05 0.05]); plot(error_FISTA_student_TV);  title('RMSE plot'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); plot(obj_FISTA_student_TV);  title('Objective plot'); colorbar;
-colormap(cmapnew); 
-%%
-% print all RMSE's
-fprintf('%s\n', '--------------------------------------------');
-fprintf('%s %.4f\n', 'RMSE for FBP reconstruction:', RMSE(FBP_2(:), phantom(:)));
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-LS reconstruction:', min(error_FISTA(:)));
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-LS-TV reconstruction:', min(error_FISTA_TV(:)));
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-GH-TV reconstruction:', min(error_FISTA_GH_TV(:)));
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-Student-TV reconstruction:', min(error_FISTA_student_TV(:)));
-%
\ No newline at end of file
diff --git a/demo/Demo2.m b/demo/Demo2.m
deleted file mode 100644
index 3c1592c..0000000
--- a/demo/Demo2.m
+++ /dev/null
@@ -1,156 +0,0 @@
-% Demonstration of tomographic reconstruction from noisy and corrupted by 
-% artifacts undersampled projection data using Students t penalty 
-% This is the missing wedge demo, run it after DemoFISTA_StudT
-
-% see ReadMe file for instructions
-% clear all
-% close all 
-
-load phantom_bone512.mat % load the phantom
-load  my_red_yellowMAP.mat % load the colormap
-% load sino1.mat; % load noisy sinogram
-
-N = 512; %  the size of the tomographic image NxN
-theta = 1:1:180; % acquisition angles (in parallel beam from 0 to Pi)
-theta_rad = theta*(pi/180); % conversion to radians
-P = 2*ceil(N/sqrt(2))+1; % the size of the detector array
-ROI = find(phantom > 0.0);
-
-add_wedges % apply the missing wedge mask
-
-%% 
-fprintf('%s\n', 'Direct reconstruction using FBP...');
-FBP_1 = iradon(MW_sino_artifacts', theta, N); 
-
-fprintf('%s %.4f\n', 'RMSE for FBP reconstruction:', RMSE(FBP_1(:), phantom(:)));
-
-figure(1); 
-% set(gcf, 'Position', get(0,'Screensize'));
-subplot_tight(1,2,1, [0.05 0.05]);  imshow(FBP_1,[-2 0.8]);  title('FBP reconstruction of noisy and corrupted by artifacts sinogram'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]);  imshow((phantom - FBP_1).^2,[0 0.1]);  title('residual: (ideal phantom - FBP)^2'); colorbar;
-colormap(cmapnew); 
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-LS without regularization...');
-clear params
-% define parameters
-params.sino = MW_sino_artifacts;
-params.N = N; % image size 
-params.angles = theta_rad; % angles in radians 
-params.iterFISTA = 132; %max number of outer iterations
-params.X_ideal = phantom; % ideal phantom
-params.ROI = ROI; % phantom region-of-interest
-params.show = 0; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-params.weights = Dweights; % statistical weighting 
-tic; [X_FISTA, error_FISTA, obj_FISTA, sinoFISTA] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-LS reconstruction:', min(error_FISTA(:)));
-
-figure(2); clf
-%set(gcf, 'Position', get(0,'Screensize'));
-subplot_tight(1,2,1, [0.05 0.05]); imshow(X_FISTA,[0 0.6]); title('FISTA-LS reconstruction'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); imshow((phantom - X_FISTA).^2,[0 0.1]);  title('residual'); colorbar;
-colormap(cmapnew); 
-figure(3); clf
-subplot_tight(1,2,1, [0.05 0.05]); plot(error_FISTA);  title('RMSE plot'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); plot(obj_FISTA);  title('Objective plot'); colorbar;
-colormap(cmapnew); 
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-LS-TV...');
-clear params
-% define parameters
-params.sino = MW_sino_artifacts;
-params.N = N; % image size 
-params.angles = theta_rad; % angles in radians 
-params.iterFISTA = 200; % max number of outer iterations
-params.lambdaTV = 5.39e-05; % regularization parameter for TV problem
-params.tol = 1.0e-04; % tolerance to terminate TV iterations
-params.iterTV = 20; % the max number of TV iterations
-params.X_ideal = phantom; % ideal phantom
-params.ROI = ROI; % phantom region-of-interest
-params.weights = Dweights; % statistical weighting 
-params.show = 0; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-tic; [X_FISTA_TV, error_FISTA_TV, obj_FISTA_TV, sinoFISTA_TV] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-LS-TV reconstruction:', min(error_FISTA_TV(:)));
-
-figure(4); clf
-subplot_tight(1,2,1, [0.05 0.05]); imshow(X_FISTA_TV,[0 0.6]); title('FISTA-LS-TV reconstruction'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); imshow((phantom - X_FISTA_TV).^2,[0 0.1]);  title('residual'); colorbar;
-colormap(cmapnew); 
-figure(5); clf
-subplot_tight(1,2,1, [0.05 0.05]); plot(error_FISTA_TV);  title('RMSE plot'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); plot(obj_FISTA_TV);  title('Objective plot'); colorbar;
-colormap(cmapnew); 
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-GH-TV...');
-clear params
-% define parameters
-params.sino = MW_sino_artifacts;
-params.N = N; % image size 
-params.angles = theta_rad; % angles in radians 
-params.iterFISTA = 250; % max number of outer iterations
-params.lambdaTV = 0.0019;  % regularization parameter for TV problem
-params.tol = 1.0e-04; % tolerance to terminate TV iterations
-params.iterTV = 20; % the max number of TV iterations
-params.X_ideal = phantom; % ideal phantom
-params.ROI = ROI; % phantom region-of-interest
-params.weights = Dweights; % statistical weighting 
-params.lambdaR_L1 = 0.002; % parameter to sparsify the "rings vector"
-params.show = 0; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-tic; [X_FISTA_GH_TV, error_FISTA_GH_TV, obj_FISTA_GH_TV, sinoFISTA_GH_TV] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-GH-TV reconstruction:', min(error_FISTA_GH_TV(:)));
-
-figure(6); clf
-subplot_tight(1,2,1, [0.05 0.05]); imshow(X_FISTA_GH_TV,[0 0.6]); title('FISTA-GH-TV reconstruction'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]);imshow((phantom - X_FISTA_GH_TV).^2,[0 0.1]);  title('residual'); colorbar;
-colormap(cmapnew); 
-
-figure(7); clf
-subplot_tight(1,2,1, [0.05 0.05]);  plot(error_FISTA_GH_TV);  title('RMSE plot'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]);  plot(obj_FISTA_GH_TV);  title('Objective plot'); colorbar;
-colormap(cmapnew); 
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-Student-TV...');
-clear params
-% define parameters
-params.sino = MW_sino_artifacts;
-params.N = N; % image size 
-params.angles = theta_rad; % angles in radians 
-params.iterFISTA = 80; % max number of outer iterations
-% params.L_const = 80000; % Lipshitz constant (can be chosen manually to accelerate convergence)
-params.lambdaTV = 0.0016;  % regularization parameter for TV problem
-params.tol = 1.0e-04; % tolerance to terminate TV iterations
-params.iterTV = 20; % the max number of TV iterations
-params.X_ideal = phantom; % ideal phantom
-params.ROI = ROI; % phantom region-of-interest
-params.weights = Dweights; % statistical weighting 
-params.fidelity = 'student'; % selecting students t fidelity
-params.show = 0; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-tic; [X_FISTA_student_TV, error_FISTA_student_TV, obj_FISTA_student_TV, sinoFISTA_student_TV] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-Student-TV reconstruction:', min(error_FISTA_student_TV(:)));
-
-figure(8); 
-set(gcf, 'Position', get(0,'Screensize'));
-subplot_tight(1,2,1, [0.05 0.05]); imshow(X_FISTA_student_TV,[0 0.6]); title('FISTA-Student-TV reconstruction'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); imshow((phantom - X_FISTA_student_TV).^2,[0 0.1]);  title('residual'); colorbar;
-colormap(cmapnew); 
-
-figure(9); 
-subplot_tight(1,2,1, [0.05 0.05]); plot(error_FISTA_student_TV);  title('RMSE plot'); colorbar;
-subplot_tight(1,2,2, [0.05 0.05]); plot(obj_FISTA_student_TV);  title('Objective plot'); colorbar;
-colormap(cmapnew); 
-%%
-% print all RMSE's
-fprintf('%s\n', '--------------------------------------------');
-fprintf('%s %.4f\n', 'RMSE for FBP reconstruction:', RMSE(FBP_2(:), phantom(:)));
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-LS reconstruction:', min(error_FISTA(:)));
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-LS-TV reconstruction:', min(error_FISTA_TV(:)));
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-GH-TV reconstruction:', min(error_FISTA_GH_TV(:)));
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-Student-TV reconstruction:', min(error_FISTA_student_TV(:)));
-%
\ No newline at end of file
diff --git a/demo/DemoRD1.m b/demo/DemoRD1.m
deleted file mode 100644
index 9a43cb5..0000000
--- a/demo/DemoRD1.m
+++ /dev/null
@@ -1,99 +0,0 @@
-% Demonstration of tomographic reconstruction from neutron tomography
-% dataset (basalt sample) using Student t data fidelity 
-clear all
-close all 
-
-% adding paths
-addpath('data/'); 
-addpath('main_func/'); 
-addpath('supp/'); 
-
-load('sino_basalt.mat') % load real neutron data
-
-size_det = size(sino_basalt, 1); % detector size
-angSize = size(sino_basalt,2); % angles dim
-recon_size = 650; % reconstruction size
-
-FBP = iradon(sino_basalt, rad2deg(angles),recon_size);
-figure; imshow(FBP , [0, 0.45]); title ('FBP reconstruction');
-
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-LS without regularization...');
-clear params
-params.sino = sino_basalt';
-params.N  = recon_size;
-params.angles = angles;
-params.iterFISTA  = 50;
-params.show = 0;
-params.maxvalplot = 0.6; params.slice = 1;
-
-tic; [X_fista] = FISTA_REC(params); toc;
-figure; imshow(X_fista , [0, 0.45]); title ('FISTA-LS reconstruction');
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-LS-TV...');
-clear params
-params.sino = sino_basalt';
-params.N  = recon_size;
-params.angles = angles;
-params.iterFISTA  = 150;
-params.lambdaTV = 0.0003; % TV regularization parameter
-params.tol = 1.0e-04;
-params.iterTV = 20;
-params.show = 1;
-params.maxvalplot = 0.6; params.slice = 1;
-
-tic; [X_fista_TV] = FISTA_REC(params); toc;
-figure; imshow(X_fista_TV , [0, 0.45]); title ('FISTA-LS-TV reconstruction');
-%%
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-GH-TV...');
-clear params
-params.sino = sino_basalt';
-params.N  = recon_size;
-params.angles = angles;
-params.iterFISTA  = 350;
-params.lambdaTV = 0.0003; % TV regularization parameter
-params.tol = 1.0e-04;
-params.iterTV = 20;
-params.lambdaR_L1 = 0.001; % Soft-Thresh L1 ring variable parameter
-params.show = 1;
-params.maxvalplot = 0.6; params.slice = 1;
-
-tic; [X_fista_GH_TV] = FISTA_REC(params); toc;
-figure; imshow(X_fista_GH_TV , [0, 0.45]); title ('FISTA-GH-TV reconstruction');
-%%
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-Student-TV...');
-clear params
-params.sino = sino_basalt';
-params.N  = recon_size;
-params.angles = angles;
-params.iterFISTA  = 350;
-params.L_const = 7000; % Lipshitz constant
-params.lambdaTV = 0.0003; % TV regularization parameter
-params.tol = 1.0e-04;
-params.iterTV = 20;
-params.fidelity = 'student'; % choosing Student t penalty
-params.show = 1;
-params.maxvalplot = 0.6; params.slice = 1;
-
-tic; [X_fistaStudentTV] = FISTA_REC(params); toc;
-figure; imshow(X_fistaStudentTV , [0, 0.45]); title ('FISTA-Student-TV reconstruction');
-%%
-
-fprintf('%s\n', 'Segmentation using OTSU method ...');
-level = graythresh(X_fista);
-Segm_FISTA = im2bw(X_fista,level);
-figure; imshow(Segm_FISTA, []); title ('Segmented FISTA-LS reconstruction');
-
-level = graythresh(X_fista_TV);
-Segm_FISTA_TV = im2bw(X_fista_TV,level);
-figure; imshow(Segm_FISTA_TV, []); title ('Segmented FISTA-LS-TV reconstruction');
-
-level = graythresh(X_fista_GH_TV);
-BW_FISTA_GH_TV = im2bw(X_fista_GH_TV,level);
-figure; imshow(BW_FISTA_GH_TV, []); title ('Segmented FISTA-GH-TV reconstruction');
-
-level = graythresh(X_fistaStudentTV);
-BW_FISTA_Student_TV = im2bw(X_fistaStudentTV,level);
-figure; imshow(BW_FISTA_Student_TV, []); title ('Segmented FISTA-Student-LS reconstruction');
\ No newline at end of file
diff --git a/demo/DemoRD2.m b/demo/DemoRD2.m
deleted file mode 100644
index a8ac2ca..0000000
--- a/demo/DemoRD2.m
+++ /dev/null
@@ -1,130 +0,0 @@
-% Demonstration of tomographic 3D reconstruction from X-ray synchrotron 
-% dataset (dendrites) using various data fidelities 
-% clear all
-% close all 
-% 
-% % adding paths
- addpath('data/'); 
- addpath('main_func/'); 
- addpath('supp/'); 
-
-load('sino3D_dendrites.mat') % load 3D normalized sinogram
-angles_rad = angles*(pi/180); % conversion to radians
-
-angSize = size(Sino3D,1); % angles dim
-size_det = size(Sino3D, 2); % detector size
-recon_size = 850; % reconstruction size
-
-FBP = iradon(Sino3D(:,:,10)', angles,recon_size);
-figure; imshow(FBP , [0, 3]); title ('FBP reconstruction');
-
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-LS without regularization...');
-clear params
-params.sino = Sino3D;
-params.N  = recon_size;
-params.angles = angles_rad;
-params.iterFISTA  = 80;
-params.precondition  = 1; % switch on preconditioning
-params.show = 0;
-params.maxvalplot = 2.5; params.slice = 10;
-
-tic; [X_fista] = FISTA_REC(params); toc;
-figure; imshow(X_fista(:,:,10) , [0, 2.5]); title ('FISTA-LS reconstruction');
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-LS-TV...');
-clear params
-params.sino = Sino3D;
-params.N  = recon_size;
-params.angles = angles_rad;
-params.iterFISTA  = 100;
-params.lambdaTV = 0.001; % TV regularization parameter for FISTA-TV
-params.tol = 1.0e-04;
-params.iterTV = 20;
-params.precondition  = 1; % switch on preconditioning
-params.show = 0;
-params.maxvalplot = 2.5; params.slice = 10;
-
-tic; [X_fista_TV] = FISTA_REC(params); toc;
-figure; imshow(X_fista_TV(:,:,10) , [0, 2.5]); title ('FISTA-LS-TV reconstruction');
-%%
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-GH-TV...');
-clear params
-params.sino = Sino3D;
-params.N  = recon_size;
-params.angles = angles_rad;
-params.iterFISTA  = 100;
-params.lambdaTV = 0.001; % TV regularization parameter for FISTA-TV
-params.tol = 1.0e-04;
-params.iterTV = 20;
-params.lambdaR_L1 = 0.001; % Soft-Thresh L1 ring variable parameter
-params.alpha_ring = 20; % to boost ring removal procedure
-params.precondition  = 1; % switch on preconditioning
-params.show = 0;
-params.maxvalplot = 2.5; params.slice = 10;
-
-tic; [X_fista_GH_TV] = FISTA_REC(params); toc;
-figure; imshow(X_fista_GH_TV(:,:,10) , [0, 2.5]); title ('FISTA-GH-TV reconstruction');
-%%
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-GH-TV-LLT...');
-clear params
-params.sino = Sino3D;
-params.N  = recon_size;
-params.angles = angles_rad;
-params.iterFISTA  = 100;
-params.lambdaTV = 0.001; % TV regularization parameter for FISTA-TV
-params.tol = 1.0e-04;
-params.iterTV = 20;
-params.lambdaHO = 35;  % regularization parameter for LLT problem
-params.tauHO = 0.00011; % time-step parameter for explicit scheme
-params.iterHO = 70; % the max number of TV iterations   
-params.lambdaR_L1 = 0.001; % Soft-Thresh L1 ring variable parameter
-params.alpha_ring = 20; % to boost ring removal procedure
-params.precondition  = 1; % switch on preconditioning
-params.show = 0;
-params.maxvalplot = 2.5; params.slice = 10;
-
-tic; [X_fista_GH_TVLLT] = FISTA_REC(params); toc;
-figure; imshow(X_fista_GH_TVLLT(:,:,10) , [0, 2.5]); title ('FISTA-GH-TV-LLT reconstruction');
-%%
-%%
-% fprintf('%s\n', 'Reconstruction using FISTA-Student-TV...');
-% %%%%<<<< Not stable with this dataset! Requires more work >>>> %%%%%
-% clear params
-% params.sino = Sino3D(:,:,15);
-% params.N  = 950;
-% params.angles = angles_rad;
-% params.iterFISTA  = 150;
-% params.L_const = 30; % Lipshitz constant
-% params.lambdaTV = 0.009; % TV regularization parameter for FISTA-TV
-% params.tol = 1.0e-04;
-% params.iterTV = 20;
-% params.fidelity = 'student'; % choosing Student t penalty
-% % params.precondition  = 1; % switch on preconditioning
-% params.show = 1;
-% params.maxvalplot = 2.5; params.slice = 1;
-% 
-% tic; [X_fistaStudentTV] = FISTA_REC(params); toc;
-% figure; imshow(X_fistaStudentTV , [0, 2.5]); title ('FISTA-Student-TV reconstruction');
-%%
-slice = 10; % if 3D reconstruction
-
-fprintf('%s\n', 'Segmentation using OTSU method ...');
-level = graythresh(X_fista(:,:,slice));
-Segm_FISTA = im2bw(X_fista(:,:,slice),level);
-figure; imshow(Segm_FISTA, []); title ('Segmented FISTA-LS reconstruction');
-
-level = graythresh(X_fista_TV(:,:,slice));
-Segm_FISTA_TV = im2bw(X_fista_TV(:,:,slice),level);
-figure; imshow(Segm_FISTA_TV, []); title ('Segmented FISTA-LS-TV reconstruction');
-
-level = graythresh(X_fista_GH_TV(:,:,slice));
-BW_FISTA_GH_TV = im2bw(X_fista_GH_TV(:,:,slice),level);
-figure; imshow(BW_FISTA_GH_TV, []); title ('Segmented FISTA-GH-TV reconstruction');
-
-level = graythresh(X_fista_GH_TVLLT(:,:,slice));
-BW_FISTA_GH_TVLLT = im2bw(X_fista_GH_TVLLT(:,:,slice),level);
-figure; imshow(BW_FISTA_GH_TVLLT, []); title ('Segmented FISTA-GH-TV-LLT reconstruction');
-%%
\ No newline at end of file