除去噪声对sar成像的影响的matlab代码
时间: 2023-08-30 08:12:10 浏览: 49
这里提供一份基于MATLAB的去噪声代码,可以用于SAR成像:
```matlab
% 读取SAR图像数据
originalImage = imread('sar_image.tif');
figure; imshow(originalImage, []);
% 添加高斯白噪声
noisyImage = imnoise(originalImage,'gaussian', 0, 0.01);
figure; imshow(noisyImage, []);
% 中值滤波去除噪声
medianFiltered = medfilt2(noisyImage, [3 3]);
figure; imshow(medianFiltered, []);
% 非局部均值去噪
denoisedImage = DenoiseImage(noisyImage, 0.05);
figure; imshow(denoisedImage, []);
% 定义非局部均值去噪函数
function noisyImage = DenoiseImage(noisyImage, sigma)
windowSize = 5;
patchSize = 7;
[h, w] = size(noisyImage);
noisyImage = double(noisyImage) / 255;
denoisedImage = zeros(h, w);
for i=1:h
for j=1:w
patch = noisyImage(max(i-floor(patchSize/2), 1):min(i+floor(patchSize/2), h),...
max(j-floor(patchSize/2), 1):min(j+floor(patchSize/2), w));
wMap = zeros(size(patch));
for k=i-floor(windowSize/2):i+floor(windowSize/2)
for l=j-floor(windowSize/2):j+floor(windowSize/2)
if (k >= 1) && (k <= h) && (l >= 1) && (l <= w)
neighborPatch = noisyImage(max(k-floor(patchSize/2), 1):min(k+floor(patchSize/2), h),...
max(l-floor(patchSize/2), 1):min(l+floor(patchSize/2), w));
diff = patch - neighborPatch;
w = exp(-sum(diff(:).^2) / (sigma^2));
wMap(k-i+floor(windowSize/2)+1, l-j+floor(windowSize/2)+1) = w;
end
end
end
wMap = wMap / sum(wMap(:));
denoisedImage(i,j) = sum(sum(wMap .* noisyImage(max(i-floor(patchSize/2), 1):min(i+floor(patchSize/2), h),...
max(j-floor(patchSize/2), 1):min(j+floor(patchSize/2), w))));
end
end
noisyImage = uint8(denoisedImage*255);
end
```
这份代码包含了中值滤波和非局部均值去噪两种方法,可以根据具体需求选择使用。其中,非局部均值去噪是一种比较常用的去噪声方法,可以在一定程度上保留图像细节,并且对于高斯白噪声有较好的去除效果。