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

In aws: Adaptive Weights Smoothing

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, [e-mail], https://www.wias-berlin.de/people/polzehl/

References

J. Polzehl, K. Papafitsoros, K. Tabelow (2020). Patch-Wise Adaptive Weights Smoothing in R, Journal of Statistical Software, 95(6), 1-27. doi:10.18637/jss.v095.i06 .

A. Buades, B. Coll and J. M. Morel (2006). A review of image denoising algorithms, with a new one. Simulation, 4, 490-530. DOI:10.1137/040616024.

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

aws documentation built on Oct. 1, 2024, 1:08 a.m.