wiener2 - 2-D adaptive noise-removal filtering

Note   The syntax wiener2(I,[m n],[mblock nblock],noise) has been removed. Use the wiener2(I,[m n],noise) syntax instead.

Syntax

J = wiener2(I,[m n],noise)
[J,noise] = wiener2(I,[m n])

Description

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.

[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 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.

Examples

For an example, see Removing Noise By Adaptive Filtering.

Algorithms

wiener2 estimates the local mean and variance around each pixel.

and

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.

References

Lim, Jae S., Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice Hall, 1990, p. 548, equations 9.44 -- 9.46.

See Also

filter2 | medfilt2

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