非局部均值滤波代码.docVIP

clear clc clf colormap(gray) % create example image ima=100*ones(100); ima(50:100,:)=50; ima(:,50:100)=2*ima(:,50:100); fs=fspecial(average); ima=imfilter(ima,fs,symmetric); % add some noise sigma=10; rima=ima+sigma*randn(size(ima)); % show it imagesc(rima) drawnow % denoise it fima=NLmeansfilter(ima,5,2,sigma); % show results clf subplot(2,2,1),imagesc(ima),title(original); subplot(2,2,2),imagesc(rima),title(noisy); subplot(2,2,3),imagesc(fima),title(filtered); subplot(2,2,4),imagesc(rima-fima),title(residuals); function [output]=NLmeansfilter(input,t,f,h) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % input: image to be filtered % t: radio of search window % f: radio of similarity window % h: degree of filtering % % Author: Jose Vicente Manjon Herrera Antoni Buades % Date: 09-03-2006 % % Implementation of the Non local filter proposed for A. Buades, B. Coll and J.M. Morel in % A non-local algorithm for image denoising % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Size of the image [m n]=size(input); % Memory for the output Output=zeros(m,n); % Replicate the boundaries of the input image input2 = padarray(input,[f f],symmetric); % Used kernel kernel = make_kernel(f); kernel = kernel / sum(sum(kernel)); h=h*h; for i=1:m for j=1:n i1 = i+ f; j1 = j+ f; W1= input2(i1-f:i1+f , j1-f:j1+f); wmax=0; average=0; sweight=0; rmin = max(i1-t,f+1); rmax = min(i1+t,m+f); smin = max(j1-t,f+1); smax = min(j1+t,n+f); for r=rmin:1:rmax for s=smin:1:smax if(r==i1 s==j1) continue; end; W2= input2(r-f:r+f , s-f:s+f); d = sum(sum(kernel.*(W1-W2).*(W1-