| Image Processing Toolbox | |
Perform two-dimensional adaptive noise-removal filtering
Syntax
J = wiener2(I,[m n],noise) [J,noise] = wiener2(I,[m n])
Description
wiener2 lowpass filters an intensity image that has been degraded by constant power additive noise. wiener2 uses a pixel-wise adaptive Wiener method based on statistics estimated from a local neighborhood of each pixel.
J = wiener2(I,[m n],noise) filters the image I using pixel-wise adaptive Wiener filtering, using neighborhoods of size m-by-n to estimate the local image mean and standard deviation. If you omit the [m n] argument, m and n default to 3. The additive noise (Gaussian white noise) power is assumed to be noise.
[J,noise] = wiener2(I,[m n]) also estimates the additive noise power before doing the filtering. wiener2 returns this estimate in noise.
Class Support
The input image I can be of class uint8, uint16, or double. The output image J is of the same class as I.
Example
Degrade and then restore an intensity image using adaptive Wiener filtering.
I = imread('saturn.tif');
J = imnoise(I,'gaussian',0,0.005);
K = wiener2(J,[5 5]);
imshow(J)
figure, imshow(K)
Algorithm
wiener2 estimates the local mean and variance around each pixel
where 
is the N-by-M local neighborhood of each pixel in the image A. wiener2 then creates a pixel-wise Wiener filter using these estimates
where 
2 is the noise variance. If the noise variance is not given, wiener2 uses the average of all the local estimated variances.
See Also
Reference
Lim, Jae S. Two-Dimensional Signal and Image Processing. Englewood Cliffs, NJ: Prentice Hall, 1990. pp. 536-540.
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