Publication
Title
Wavelet-based multicomponent image restoration
Author
Abstract
In this paper we study the restoration of multicomponent images, and more particularly, the effects of taking into account the dependencies between the image components. The used method is an expectation-maximization algorithm, which applies iteratively a deconvolution and a denoising step. It exploits the Fourier transform's economical noise representation for deconvolution, and the wavelet transform's economical representation of piecewise smooth images for denoising. The proposed restoration procedure performs wavelet shrinkage in a Bayesian denoising framework by applying multicomponent probability density models for the wavelet coefficients that fully account for the intercomponent correlations. In the experimental section, we compare our multicomponent procedures to its single-component counterpart. The results show that the methods using a multicomponent model and especially the one using the Gaussian scale mixture model, perform better than the single-component procedure.
Language
English
Source (book)
Wavelet Applications in Industrial Processing: 5: part of SPIE Optics East, Boston, Mass., 9-12 September
Publication
s.l. : 2007
ISBN
978-0-8194-6923-6
Volume/pages
p. J7630,1-J7630,10
ISI
000253269200014
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 08.10.2008
Last edited 05.05.2017
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