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 |
searchhw |
Half width of search area (size of search area is |
pd |
If |
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, 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