In this paper a wavelet based model for image de-noising is presented. Wavelet coefficients are modelled as waves that grow while dilating along scales. The model establishes a precise link between corresponding modulus maxima in the wavelet domain and then allows to predict wavelet coefficients at each scale from the first one. This property combined with the theoretical results about the characterization of singularities in the wavelet domain enables to discard noise. Significant structures of the image are well recovered while some annoying artifacts along image edges are reduced. Some experimental results show that the proposed approach outperforms the most recent and effective wavelet based denoising schemes.
%0 Journal Article
%1 citeulike:13052132
%A Bruni, Vittoria
%A Piccoli, Benedetto
%A Vitulano, Domenico
%D 2008
%J Electronic Letters on Computer Vision and Image Analysis
%K 68t45-machine-vision-scene-understanding 65d19-computational-issues-in-computer-and-robotic-vision 42c40-wavelets-and-other-special-systems
%N 2
%P 36--53
%T Wavelets and partial differential equations for image denoising
%U http://elcvia.cvc.uab.es/article/view/147
%V 6
%X In this paper a wavelet based model for image de-noising is presented. Wavelet coefficients are modelled as waves that grow while dilating along scales. The model establishes a precise link between corresponding modulus maxima in the wavelet domain and then allows to predict wavelet coefficients at each scale from the first one. This property combined with the theoretical results about the characterization of singularities in the wavelet domain enables to discard noise. Significant structures of the image are well recovered while some annoying artifacts along image edges are reduced. Some experimental results show that the proposed approach outperforms the most recent and effective wavelet based denoising schemes.
@article{citeulike:13052132,
abstract = {{In this paper a wavelet based model for image de-noising is presented. Wavelet coefficients are modelled as waves that grow while dilating along scales. The model establishes a precise link between corresponding modulus maxima in the wavelet domain and then allows to predict wavelet coefficients at each scale from the first one. This property combined with the theoretical results about the characterization of singularities in the wavelet domain enables to discard noise. Significant structures of the image are well recovered while some annoying artifacts along image edges are reduced. Some experimental results show that the proposed approach outperforms the most recent and effective wavelet based denoising schemes.}},
added-at = {2017-06-29T07:13:07.000+0200},
author = {Bruni, Vittoria and Piccoli, Benedetto and Vitulano, Domenico},
biburl = {https://www.bibsonomy.org/bibtex/238ac00eba3f7ca0a25eb2672cb8305da/gdmcbain},
citeulike-article-id = {13052132},
citeulike-attachment-1 = {bruni_08_wavelets.pdf; /pdf/user/gdmcbain/article/13052132/950345/bruni_08_wavelets.pdf; 4dd4298e3fb58ee013882549f98e8484d5f4e6fb},
citeulike-linkout-0 = {http://elcvia.cvc.uab.es/article/view/147},
file = {bruni_08_wavelets.pdf},
interhash = {6394ef616db33f8d35088659a93c1d97},
intrahash = {38ac00eba3f7ca0a25eb2672cb8305da},
issn = {1577-5097},
journal = {Electronic Letters on Computer Vision and Image Analysis},
keywords = {68t45-machine-vision-scene-understanding 65d19-computational-issues-in-computer-and-robotic-vision 42c40-wavelets-and-other-special-systems},
number = 2,
pages = {36--53},
posted-at = {2014-02-18 00:49:19},
priority = {2},
timestamp = {2020-01-15T04:37:52.000+0100},
title = {{Wavelets and partial differential equations for image denoising}},
url = {http://elcvia.cvc.uab.es/article/view/147},
volume = 6,
year = 2008
}