function?DenoisedImg=NLmeans（IdsDsh）
%I:含噪聲圖像
%ds:鄰域窗口半徑
%Ds:搜索窗口半徑
%h:高斯函數平滑參數
%DenoisedImg：去噪圖像
I=double（I）;
[mn]=size（I）;
DenoisedImg=zeros（mn）;
PaddedImg?=?padarray（I[dsds]‘symmetric‘‘both‘）;
kernel=ones（2*ds+12*ds+1）;
kernel=kernel./（（2*ds+1）*（2*ds+1））;
h2=h*h;
for?i=1:m
????for?j=1:n
????????i1=i+ds;
????????j1=j+ds;
????????W1=PaddedImg（i1-ds:i1+dsj1-ds:j1+ds）;%鄰域窗口1
????????wmax=0;
????????average=0;
????????sweight=0;
????????%%搜索窗口
????????rmin?=?max（i1-Dsds+1）;
????????rmax?=?min（i1+Dsm+ds）;
????????smin?=?max（j1-Dsds+1）;
????????smax?=?min（j1+Dsn+ds）;
????????for?r=rmin:rmax
????????????for?s=smin:smax
????????????????if（r==i1&&s==j1）
????????????????continue;
????????????????end
????????????????W2=PaddedImg（r-ds:r+dss-ds:s+ds）;%鄰域窗口2
????????????????Dist2=sum（sum（kernel.*（W1-W2）.*（W1-W2）））;%鄰域間距離
????????????????w=exp（-Dist2/h2）;
????????????????if（w>wmax）
????????????????????wmax=w;
????????????????end
????????????????sweight=sweight+w;
????????????????average=average+w*PaddedImg（rs）;
????????????end
????????end
????????average=average+wmax*PaddedImg（i1j1）;%自身取最大權值
????????sweight=sweight+wmax;
????????DenoisedImg（ij）=average/sweight;
????end
end

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件?????3039232??2017-12-25?21:02??Denoise.ppt
?????目錄???????????0??2017-12-25?20:55??NLM\
?????文件???????66614??2017-12-07?21:17??NLM\LENA.BMP
?????文件????????1296??2017-01-03?10:44??NLM\NLmeans.m
?????文件?????????170??2017-12-25?20:51??NLM\test_nlm.m