|Image Processing Toolbox User's Guide|
Using Adaptive Filtering
wiener2 function applies a Wiener filter (a type of linear filter) to an image adaptively, tailoring itself to the local image variance. Where the variance is large,
wiener2 performs little smoothing. Where the variance is small,
wiener2 performs more smoothing.
This approach often produces better results than linear filtering. The adaptive filter is more selective than a comparable linear filter, preserving edges and other high-frequency parts of an image. In addition, there are no design tasks; the
wiener2 function handles all preliminary computations and implements the filter for an input image.
wiener2, however, does require more computation time than linear filtering.
wiener2 works best when the noise is constant-power ("white") additive noise, such as Gaussian noise. The example below applies
wiener2 to an image of Saturn that has had Gaussian noise added. For an interactive demonstration of filtering to remove noise, try running
RGB = imread('saturn.png'); I = rgb2gray(RGB); J = imnoise(I,'gaussian',0,0.005); K = wiener2(J,[5 5]); imshow(J) figure, imshow(K)
Noisy Version (left) and Filtered Version (right)
|Using Median Filtering||Region-Based Processing|
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