NLmeansfilter.m_非局部均值滤波_图像去噪_

function [output]=NLmeansfilter(input,f,t,h) [m n]=size(input); %t:搜索框半径；f:相似框半径；h:滤波频率百分比 Output=zeros(m,n); input2 = padarray(input,[f f],'symmetric'); %将边缘对称折叠上去 % f :加宽的宽度值 kernel = make_kernel(f); %计算得到一个高斯核，用于后续的计算 kernel = kernel / sum(sum(kernel)); %sum：对矩阵k的每一列求和，k表示矩阵k的总和 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); %确定相似框的边长，其实可以在代入数据的时候就设置搜索框和边界框边长的大小关系，就不用求最大最小值了 rmax = min(i1+t,m+f); %这段主要是控制不超出索引值 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-W2))); w=exp(-d/h); if w>wmax wmax=w; end sweight = sweight + w; average = average + w*input2(r,s); end end average = average + wmax*input2(i1,j1); sweight = sweight + wmax; if sweight > 0 output(i,j) = average / sweight; else output(i,j) = input(i,j); end end end function [kernel] = make_kernel(f) %这是一个高斯核 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;