A Joint Inter- and Intrascale Statistical Model for Bayesian Wavelet Based Image Denoising (2002)
| Venue: | IEEE Trans. Image Proc |
| Citations: | 38 - 5 self |
BibTeX
@ARTICLE{Pizurica02ajoint,
author = {Aleksandra Pizurica and Wilfried Philips and Ignace Lemahieu and Marc Acheroy},
title = {A Joint Inter- and Intrascale Statistical Model for Bayesian Wavelet Based Image Denoising},
journal = {IEEE Trans. Image Proc},
year = {2002},
volume = {11},
pages = {545--557}
}
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Abstract
This paper presents a new wavelet-based image denoising method, which extends a recently emerged "geometrical" Bayesian framework. The new method combines these criteria for distinguishing supposedly useful coefficient from noise coefficient magnit:q54 tgni evolut47 across scales and spatA5 clust:q5A of large coefficients near image edges. These three crit546 are combined in a Bayesian framework. The spatD5 clust:q5] propert:5 are expressed in a prior model. Thest6[]A:q5D propertAA concerning coefficient magnit[:q andt:55 evolut4[ across scales are expressed in a joint condit:q]6 model. The three main noveltAA with respect to relat[ approaches are:(1)t he int760C7:q]0056: of wavelet coefficient are st0057:q]005 charact:q]55C and different local crit44C for dist]6:q]55C5 useful coefficient from noise are evaluat]6 (2) a joint condit:q]7 model is introduced, and (3) a novel anisot:q]7 Markov Random Field prior model is proposed. The results demonstrate an improved denoising performance over related earlier techniques.
