matlab-基于OMP和KSVD算法的图像去噪算法matlab仿真,包括图像分块,字典,重构,OMP,KSVD等步骤-源码

function [recovImg, costTime, psnr, prePSNR] = ImgDenoise(orgImg, sigma, method) % function ImgDenoise %% 读图像文件，产生噪声 % orgImg = imread('lena256.bmp'); % method = 'OMP'; % sigma = 25; orgImg = double(orgImg); sigmaImg = std2(orgImg); % figure(1), imshow(uint8(orgImg)), title('原始图像 '), drawnow; [a,b] = size(orgImg); lam = 0; n = 8; C = 2*sigma^2*(1+sigma^2/sigmaImg^2); C = 1/C; ranTime = 10; distance = 4; noisyMtx = sigma * randn(a,b); noisyImg = orgImg + noisyMtx; % figure(2), imshow(uint8(noisyImg)), title('噪声图像 '), drawnow; % noisyImg = imresize(noisyImg, 2); [a, b] = size(noisyImg); %% 生成字典 K = 256; subDicMtx = zeros(n,sqrt(K)); for k=0:1:sqrt(K)-1, V=cos((0:1:n-1)*k*pi/sqrt(K)); if k>0, V=V-mean(V); end; subDicMtx(:,k+1)=V/norm(V); end; dicMtx=kron(subDicMtx,subDicMtx); dicMtx=dicMtx * diag(1./sqrt(sum(dicMtx.*dicMtx))); % dicImg = DicToImg(dicMtx, n, n); % dicImg = imresize(dicImg, 2); % figure(3), imshow(dicImg,[]), title('初始字典'), drawnow; %% 对噪声图像分块 noisyPatchs = zeros(n*n, ceil(1/distance*(a-n+1))*ceil(1/distance*(b-n+1))); count = 0; for i = 1:distance:a-n+1 for j = 1:distance:b-n+1 count = count + 1; imgPatch = noisyImg(i:i+n-1, j:j+n-1); noisyPatchs(:, count) = imgPatch(:); end end %% 重构分块数据，也是降噪过程 reSparRepMtx = zeros(size(dicMtx, 2), count); tic; if (strcmp(method, 'OMP')) for i = 1:1:count reSparRepMtx(:,i) = OMP2(dicMtx, noisyPatchs(:,i), sigma); end elseif (strcmp(method, 'RandOMP')) sparRepcolums = zeros(size(dicMtx, 2), ranTime); for i = 1:1:count for j =1:1:ranTime sparRepcolums(:, j) = RandOMP2(dicMtx, noisyPatchs(:,i), sigma, C); end reSparRepMtx(:,i) = mean(sparRepcolums, 2); end end costTime = toc; recovPatchs = dicMtx * reSparRepMtx; %% 分块数据重组成图像 [recovImg, weightMtx] = ReconstructImg(recovPatchs, distance, a, b, n); recovImg = recovImg./weightMtx; % recovImg = imresize(recovImg, 0.5); figure(4), imshow(uint8(recovImg)), title('重构图像 '), drawnow; %% 计算恢复前后的psnr % preErrSum = sum(sum(abs(noisyImg-orgImg).^2)); % prePSNR = 10*log10(255*255/(preErrSum/256/256)); prePSNR=1; errSum = sum(sum(abs(recovImg-orgImg).^2)); psnr = 10*log10(255*255/(errSum/256/256))