【图像重建】基于Split Bregman实现稀疏图像重建附matlab代码

%          test_mrics.m by Tom Goldstein  (tagoldst@math.ucla.edu)%   This file is meant to demonstrate how to properly use mrics.m%   When this script is run, it will first build a simple test image.  The%   method then builds a sampling matrix, R, with entries randomly chosen %   to be 1 or 0.  The compressed sensing data is then computed using the%   folrmula F = R.*fft2(image).  Gaussian noisy is added to the CS data.%   Finally, the mrics method is used to reconstruct the image form the%   sub-sampled K-Space data.  N = 128; % The image will be NxNsparsity = .25; % use only 30% on the K-Space data for CS   % build an image of a squareimage = zeros(N,N);image(N/4:3*N/4,N/4:3*N/4)=255;  % build the sampling matrix, RR = rand(N,N);R = R<sparsity; % Form the CS dataF = R.*fft2(image);% Recover the imagerecovered = mrics(R,F, 1, 1, 1e-5,10, 4);% build a figure to display resultsfigure;subplot(2,2,1);imagesc(abs(image)); colormap('gray');title('Original');subplot(2,2,2);imagesc(abs(R)); colormap('gray');title('R');subplot(2,2,3);imagesc(abs(ifft2(F))); colormap('gray');title('Set unknown to 0');subplot(2,2,4);imagesc(abs(recovered)); colormap('gray');title('Split Bregman Recovery');