基于Matlab实现峰值信噪比 (PSNR) 和结构相似性 (SSIM)

function output = BaysShrink(x,wv,L) % IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 9, NO. 9, SEPTEMBER 2000 % Adaptive Wavelet Thresholding for Image Denoising and Compression % S. Grace Chang, Student Member, IEEE, Bin Yu, Senior Member, IEEE, and % Martin Vetterli, Fellow, IEEE % ----------------------------------------------liujiabin 2010.5.12 % input: % x: 2-D signal matrix m by n % wv: filtertype, the type of wavelet(filterbank) used % L: number of levels %output: % output: 2-D signal matrix m by n. % % noise = randn(size(f)); % noise = noise/sqrt(mean2(noise.^2)); % % % add Gaussian noise % g = double(f)+ sigma*noise; % bayes soft thresholding % the level of decomposition %%%%%%%%%%%%%%%%%%%%% %Denoising using Bayes soft thresholding %Note: Figure window 1 displays the original image, fig 2 the noisy img %Define the type of wavelet(filterbank) used and the number of scales in the wavelet decomp %Doing the wavelet decomposition [C,S]=wavedec2(x,L,wv); st=(S(1,1)^2)+1; bayesC=[C(1:st-1),zeros(1,length(st:1:length(C)))]; var=length(C)-S(size(S,1)-1,1)^2+1; %Calculating sigmahat sigmahat=median(abs(C(var:length(C))))/0.6745; for jj=2:size(S,1)-1 %for the H detail coefficients coefh=C(st:st+S(jj,1)^2-1); thr=bayes(coefh,sigmahat); bayesC(st:st+S(jj,1)^2-1)=sthresh(coefh,thr); st=st+S(jj,1)^2; % for the V detail coefficients coefv=C(st:st+S(jj,1)^2-1); thr=bayes(coefv,sigmahat); bayesC(st:st+S(jj,1)^2-1)=sthresh(coefv,thr); st=st+S(jj,1)^2; %for Diag detail coefficients coefd=C(st:st+S(jj,1)^2-1); thr=bayes(coefd,sigmahat); bayesC(st:st+S(jj,1)^2-1)=sthresh(coefd,thr); st=st+S(jj,1)^2; end %Reconstructing the image from the Bayes-thresholded wavelet coefficients bayespic=waverec2(bayesC,S,wv); output = bayespic;