function [output]=NLmeans(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;
for i=1:m
for j=1:n
time=0;
tic
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;
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);
rmin=i1-t;
rmax=i1+t;
smin=j1-t;
smax=j1+t;
v=0;
for r=rmin:1:rmax %大窗口
for s=smin:1:smax
v=v+1;
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
end
sweight = sweight + w; %大窗口中的权重和
average = average + w*input2(r,s);
end
end
average = average + wmax*input2(i1,j1);
sweight = sweight + wmax;
time=toc;
if sweight > 0
output(i,j) = average / sweight;
else
output(i,j) = input(i,j);
end
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;
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