pnonlocalmeanfiltering

Performs a p non local mean filtering on image.

Synopsis

pnonlocalmeanfiltering sigma p nb_iter connectivity [im_in|-] [im_out|-]

Description

Performs a p non local mean filtering on image im_in. Let $f$ be im_in, this algorithm performs iteratively:
$f(u)^{t+1} = \sum_{v ~ u}{w(u, v)^{p / 2} |f^t(v) - f^t(u)|^{p - 2} f(v)} / \sum_{v ~ u}{ w(u, v)^{p / 2} |f^t(v) - f^t(u)|^{p - 2}}$
with $u$ a pixel location, $v$ a neighbor of $u$, $w(u, v)$ the weight between $u$ and $v$ (which can be a similarity measure, a distance, etc.). The parameter p can be 1, 2, or any.

We compute weights between a pixel value and its neighbors (here with an exponential similarity measure):
$w(u, v) = \exp^{-||f(v) - f(u)||^2 / \sigma^2}$.

Parameters

sigma: sigma used for the similarity measure. The greater the sigma value, the lower the filtering.

p: the p-laplacian dimension (1, 2, ..).

nb_iter: number of iteration of the algorithm.

connectivity: neighborhood, can be 4 or 8.

Inputs

im_in: a 2D image.

Outputs

im_out: an image of the same type as the input image.

Result

Returns SUCCESS or FAILURE.

Example

Applies a non local mean filter to tangram.pan, with 8-connectivity neighborhood, p=2, 10 iterations, and sigma = 15:
```
pnonlocalmeanfiltering 15 2 10 8 tangram.pan out.pan
```

C++ prototype

template Errc lplRegularization(Imx2d &imgIn, Imx2d &imgOut, float sigma, int connectivity, IRunThroughImage * imageRunner, IPerformReg * regPerformer);

Version française

Filtrage moyenneur non local.

Author: Matthieu Toutain