function [f,noise] = mywiener2(g, nhood, noise) 
if (nargin<3) noise = []; 
end
% Estimate the local mean of f. 
localMean = filter2(ones(nhood), g) / prod(nhood); 
% Estimate of the local variance of f. 
localVar = filter2(ones(nhood), g.^2) / prod(nhood) - localMean.^2; 
% Estimate the noise power if necessary. 
if (isempty(noise))
    noise = mean2(localVar); 
end
% Compute result % f = localMean + (max(0, localVar - noise) ./ ...
%       max(localVar, noise) .* (g - localMean); 
% % Computation is split up to minimize use of memory for temp arrays.
f = g - localMean; 
g = localVar - noise; 
g = max(g, 0); 
f = localMean + ((f ./ max(localVar, noise)) .* g);

 

RGB = imread('lena.jpg');
I = rgb2gray(RGB);
%I = I(601:1000,1:600);
J = imnoise(I,'gaussian',0,0.005);
J = im2double(J);
K = mywiener2(J,[5 5]);%%[5,5]領域
figure; imshow(I), title('original image'); 
figure; subplot(1,2,1), subimage(J), title('noised image'); 
subplot(1,2,2), subimage(K), title('denoised image');

![在這裡插入圖片描述](https://img-blog.csdn.net/20181023104612421?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMjQ0NDM1/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70)