The Wiener filter requires a prior knowledge of the power spectral density of original image which is unavailable in practice.

`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.

The `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.

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