基于matlab 非局部均值（NLM）滤波图像去噪

function [output]=filter(input,t,f,h) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % 输入: 待平滑的图像 % t: 搜索窗口半径 % f: 相似性窗口半径 % h: 平滑参数 % % nlmeans(ima,5,2,sigma); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 图像大小 [m n]=size(input); % 输出 Output=zeros(m,n); input2 = padarray(input,[f+t f+t],'symmetric');%边界作对称处理 % 高斯核 kernel = make_kernel(f); kernel = kernel / sum(sum(kernel)); h=h*h; beta=0.001; dt=1/(2*1+4); for i=1:m for j=1:n i1 = i+ f+t;%原始图像的像素位置 （中心像素） j1 = j+ f+t; W1= input2(i1-f:i1+f , j1-f:j1+f);%小窗口 wmax=0; Rmax=0;Smax=0; N=ones(1,5); N(:,1)=input2(i1,j1-1); %四个邻域 N(:,2)=input2(i1,j1+1); N(:,3)=input2(i1-1,j1); N(:,4)=input2(i1+1,j1); u=input2(i1,j1); rmin=i1-t; rmax=i1+t; smin=j1-t; smax=j1+t; for r=rmin:1:rmax %大窗口 for s=smin:1:smax if(r==i1 && s==j1) continue; end; W2= input2(r-f:r+f , s-f:s+f); %大搜索窗口中的小相似性窗口 d = sum(sum(kernel.*(W1-W2).*(W1-W2))); w=exp(-d/h); %权重 if w>wmax wmax=w; %求最大权重 Rmax=r; Smax=s; end end end N(:,5)=input2(Rmax,Smax); num=(N-u)./(((N-u).^2 + beta).^(1/2)) ; wei=weight(N,u,2); output(i,j) = input2(i1,j1)+dt*sum(num.*wei); end end function [kernel] = make_kernel(f) %核函数 kernel=zeros(2*f+1,2*f+1); for d=1:f value= 1 / (2*d+1)^2 ; for i=-d:d for j=-d:d kernel(f+1-i,f+1-j)= kernel(f+1-i,f+1-j) + value ; end end end kernel = kernel ./ f; function [weight] = make_weight(ul,uk,h1) %权重函数 ex=abs(ul-uk)/h1 weight=exp(-ex.*ex);