2-D adaptive noise-removal filtering
wiener2(I,[m n],[mblock nblock],noise) has
been removed. Use the
wiener2(I,[m n],noise) syntax
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.
a pixelwise adaptive Wiener method based on statistics estimated from
a local neighborhood of each pixel.
J = wiener2(I,[m n],noise) filters
I using pixelwise adaptive Wiener filtering,
using neighborhoods of size
estimate the local image mean and standard deviation. If you omit
[m n] argument,
to 3. The additive noise (Gaussian white noise) power is assumed to
[J,noise] = wiener2(I,[m n]) also
estimates the additive noise power before doing the filtering.
this estimate in
The input image
I is a two-dimensional image
double. The output image
of the same size and class as
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
creates a pixelwise Wiener filter using these estimates,
where ν2 is the noise variance.
If the noise variance is not given,
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.