Natural image denoising using evolved local adaptive filters
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Yan:2014:SP,
-
author = "Ruomei Yan and Ling Shao and Li Liu and Yan Liu",
-
title = "Natural image denoising using evolved local adaptive
filters",
-
journal = "Signal Processing",
-
year = "2014",
-
volume = "103",
-
pages = "36--44",
-
month = oct,
-
keywords = "genetic algorithms, genetic programming, Image
denoising, Bilateral filter",
-
ISSN = "0165-1684",
-
URL = "http://www.sciencedirect.com/science/article/pii/S0165168413004556",
-
DOI = "doi:10.1016/j.sigpro.2013.11.019",
-
size = "9 pages",
-
abstract = "The coefficients in previous local filters are mostly
heuristically optimised, which leads to artifacts in
the denoised image when the optimization is not
adaptive enough to the image content. Compared to
parametric filters, learning-based denoising methods
are more capable of tackling the conflicting problem of
noise reduction and artifact suppression. In this
paper, a patch-based Evolved Local Adaptive (ELA)
filter is proposed for natural image denoising. In the
training process, a patch clustering is used and the
genetic programming (GP) is applied afterwards for
determining the optimal filter (linear or nonlinear in
a tree structure) for each cluster. In the testing
stage, the optimal filter trained beforehand by GP will
be retrieved and employed on the input noisy patch. In
addition, this adaptive scheme can be used for
different noise models. Extensive experiments verify
that our method can compete with and outperform the
state-of-the-art local denoising methods in the
presence of Gaussian or salt-and-pepper noise.
Additionally, the computational efficiency has been
improved significantly because of the separation of the
offline training and the online testing processes.",
- }
Genetic Programming entries for
Ruomei Yan
Ling Shao
Li Liu
Fiona Yan Liu
Citations