R: NLMeans filter in 1D/2D/3D

nlmeans {aws} | R Documentation

## NLMeans filter in 1D/2D/3D

### Description

Implements the Non-Local-Means Filter of Buades et al 2005

### Usage

    nlmeans(x, lambda, sigma, patchhw = 1, searchhw = 7, pd = NULL)

### Arguments

`x` | 1, 2 or 3-dimensional array of obseved response (image intensity) data.
`lambda` | scale factor for kernel in image space.
`sigma` | error standard deviation (for additive Gaussian errors).
`patchhw` | Half width of patches in each dimension (patchsize is `(2*patchhw+1)^d` for d-dimensional array).
`searchhw` | Half width of search area (size of search area is `(2searchhw+1)^d` for d-dimensional array)).
`pd` | If `pd < (2*patchhw+1)^d` use `pd` principal components instead of complete patches.

### Details

The implementation follows the description of the Non-Local-Means Filter of Buades et al 2005 on http://www.numerical-tours.com/matlab/denoisingadv_6_nl_means/#biblio that incorporates dimension reduction for patch comparisons by PCA.

### Value

A list of class `"nlmeans"` with components

`theta` | Denoised array
`lambda` | Scale parameter used
`sigma` | The error standard deviation
`patchhw` | Half width of patches
`pd` | Effective patchsize used
`searchhw` | Half width of search area

### Note

use `setCores='number of threads'` to enable parallel execution.

### Author(s)

Joerg Polzehl, polzehl@wias-berlin.de, http://www.wias-berlin.de/people/polzehl/

### References

A. Buades, B. Coll and J. M. Morel (2006). A review of image denoising algorithms, with a new one. Simulation, 4, 490-530.

http://www.numerical-tours.com/matlab/denoisingadv_6_nl_means/#biblio

[Package aws version 2.2-1 Index]