图像稀疏表示MATLAB

function Q=M_SPA(img); %test code for %img=imread('RMB100_01.bmp');%256*256*3 %img=rgb2gray(img); sigma=20; imshow(img,[]); title('original image'); img=double(img); [r,c]=size(img); block_size=8; codebook_size=16;%256; C=1.15; sigma=20; [r,c]=size(img); img=img+randn(r,c)*sigma imshow(img,[]); title('noise_image'); %noise_img=img+randn(r,c)*sigma; %img=noise_img; %imshow(img,[]); %title('noise_image'); %noise_img=img+noise; %imshow(noise_img,[]); %title('original noise_image'); patches=im2col(img,[block_size,block_size],'sliding'); %imshow(patches,[]); %title('dictionary'); errGoal=C*sigma; %A=OMP(patches,img,c); %[D,coef]=MOD(img,codebook_size,errGoal); [D,coef]=KSVD(img,codebook_size,errGoal); imshow(D,[]); title('dictionary'); length(find(coef>0)) length(find(coef<0)) length(find(coef==0)) [rows,cols]=find(coef==0) %recpatches=D*coef; img=D*coef; recpatches=im2col(img,[block_size,block_size],'sliding'); %figure; %imshow(uint8(D),[]); %title('denoised image'); ind=1:size(patches,2); [r,w]=size(img); IMout=zeros(r,w); Weight=zeros(r,w); [rows,cols] = ind2sub(size(img)-block_size+1,ind); for i=1:length(cols) row=rows(i); col=cols(i); Patch_out=reshape(recpatches(:,i),block_size,block_size); IMout(row:row+block_size-1,col:col+block_size-1)=... IMout(row:row+block_size-1,col:col+block_size-1)+Patch_out; Weight(row:row+block_size-1,col:col+block_size-1)=... Weight(row:row+block_size-1,col:col+block_size-1)+ones(block_size); end denoise_img=IMout./Weight; Q=fix(denoise_img) figure; imshow(Q,[]); title('denoised image');