2-D adaptive noise-removal filtering
The syntax wiener2(I,[m n],[mblock nblock],noise) has been removed. Use the wiener2(I,[m n],noise) syntax instead.
J = wiener2(I,[m n],noise)
[J,noise] = wiener2(I,[m n])
wiener2 lowpass-filters a grayscale image that has been degraded by constant power additive noise. wiener2 uses a pixelwise 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 pixelwise 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.
The input image I is a two-dimensional image of class uint8, uint16, int16, single, or double. The output image J is of the same size and class as I.
For an example, see Remove Noise By Adaptive Filtering.
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 pixelwise Wiener filter using these estimates,
where ν2 is the noise variance. If the noise variance is not given, wiener2 uses the average of all the local estimated variances.
 Lim, Jae S., Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice Hall, 1990, p. 548, equations 9.26, 9.27, and 9.29.