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-% 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
-%%
-% compile MEX-files ones
-% cd ..
-% cd main_func
-% compile_mex
-% cd .. 
-% cd demos
-%%
-
-close all;clc;clear all;
-% adding paths
-addpath('../data/'); 
-addpath('../main_func/'); addpath('../main_func/regularizers_CPU/');
-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);
-
-% using ASTRA to set the projection geometry
-% potentially parallel geometry can be replaced with a divergent one
-Z_slices = 1;
-det_row_count = Z_slices;
-proj_geom = astra_create_proj_geom('parallel3d', 1, 1, det_row_count, P, theta_rad);
-vol_geom = astra_create_vol_geom(N,N,Z_slices);
-
-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-PWLS without regularization...');
-clear params
-% define parameters
-params.proj_geom = proj_geom; % pass geometry to the function
-params.vol_geom = vol_geom;
-params.sino = sino_zing_rings; % sinogram
-params.iterFISTA = 45; %max number of outer iterations
-params.X_ideal = phantom; % ideal phantom
-params.ROI = ROI; % phantom region-of-interest
-params.show = 1; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-params.weights = Dweights; % statistical weighting 
-tic; [X_FISTA, output] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-PWLS reconstruction is:', min(error_FISTA(:)));
-error_FISTA = output.Resid_error; obj_FISTA = output.objective;
-
-figure(2); clf
-%set(gcf, 'Position', get(0,'Screensize'));
-subplot(1,2,1, [0.05 0.05]); imshow(X_FISTA,[0 0.6]); title('FISTA-PWLS reconstruction'); colorbar;
-subplot(1,2,2, [0.05 0.05]); imshow((phantom - X_FISTA).^2,[0 0.1]);  title('residual'); colorbar;
-colormap(cmapnew); 
-figure(3); clf
-subplot(1,2,1, [0.05 0.05]); plot(error_FISTA);  title('RMSE plot'); colorbar;
-subplot(1,2,2, [0.05 0.05]); plot(obj_FISTA);  title('Objective plot'); colorbar;
-colormap(cmapnew); 
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-PWLS-TV...');
-clear params
-% define parameters
-params.proj_geom = proj_geom; % pass geometry to the function
-params.vol_geom = vol_geom;
-params.sino = sino_zing_rings;
-params.iterFISTA = 45; % max number of outer iterations
-params.Regul_LambdaTV = 0.0015; % regularization parameter for TV problem
-params.X_ideal = phantom; % ideal phantom
-params.ROI = ROI; % phantom region-of-interest
-params.weights = Dweights; % statistical weighting 
-params.show = 1; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-tic; [X_FISTA_TV, output] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-PWLS-TV reconstruction is:', min(error_FISTA_TV(:)));
-error_FISTA_TV = output.Resid_error; obj_FISTA_TV = output.objective;
-
-figure(4); clf
-subplot(1,2,1, [0.05 0.05]); imshow(X_FISTA_TV,[0 0.6]); title('FISTA-PWLS-TV reconstruction'); colorbar;
-subplot(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(1,2,1, [0.05 0.05]); plot(error_FISTA_TV);  title('RMSE plot'); colorbar;
-subplot(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.proj_geom = proj_geom; % pass geometry to the function
-params.vol_geom = vol_geom;
-params.sino = sino_zing_rings;
-params.iterFISTA = 50; % max number of outer iterations
-params.Regul_LambdaTV = 0.0015;  % regularization parameter for TV problem
-params.X_ideal = phantom; % ideal phantom
-params.ROI = ROI; % phantom region-of-interest
-params.weights = Dweights; % statistical weighting 
-params.Ring_LambdaR_L1 = 0.002; % parameter to sparsify the "rings vector"
-params.Ring_Alpha = 20; % to accelerate ring-removal procedure
-params.show = 0; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-tic; [X_FISTA_GH_TV, output] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-GH-TV reconstruction is:', min(error_FISTA_GH_TV(:)));
-error_FISTA_GH_TV = output.Resid_error; obj_FISTA_GH_TV = output.objective;
-
-figure(6); clf
-subplot(1,2,1, [0.05 0.05]); imshow(X_FISTA_GH_TV,[0 0.6]); title('FISTA-GH-TV reconstruction'); colorbar;
-subplot(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(1,2,1, [0.05 0.05]);  plot(error_FISTA_GH_TV);  title('RMSE plot'); colorbar;
-subplot(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.proj_geom = proj_geom; % pass geometry to the function
-params.vol_geom = vol_geom;
-params.sino = sino_zing_rings;
-params.iterFISTA = 55; % max number of outer iterations
-params.L_const = 0.1; % Lipshitz constant (can be chosen manually to accelerate convergence)
-params.Regul_LambdaTV = 0.00152;  % regularization parameter for TV problem
-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 = 1; % visualize reconstruction on each iteration
-params.slice = 1; params.maxvalplot = 0.6; 
-params.initilize = 1; % warm start with SIRT
-tic; [X_FISTA_student_TV, output] = FISTA_REC(params); toc; 
-
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-Student-TV reconstruction is:', min(error_FISTA_student_TV(:)));
-error_FISTA_student_TV = output.Resid_error; obj_FISTA_student_TV = output.objective;
-
-figure(8); 
-set(gcf, 'Position', get(0,'Screensize'));
-subplot(1,2,1, [0.05 0.05]); imshow(X_FISTA_student_TV,[0 0.6]); title('FISTA-Student-TV reconstruction'); colorbar;
-subplot(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(1,2,1, [0.05 0.05]); plot(error_FISTA_student_TV);  title('RMSE plot'); colorbar;
-subplot(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_1(:), phantom(:)));
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-PWLS reconstruction:', min(error_FISTA(:)));
-fprintf('%s %.4f\n', 'Min RMSE for FISTA-PWLS-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