Paper Info
Title
Multicomponent image restoration, an experimental study
Abstract
In this paper, we study the problem of restoring multicomponent images. In particular, we investigate the effects of accounting for the correlation between the image components on the deconvolution and denoising steps. The proposed restoration is a 2-step procedure, comprising a shrinkage in the Fourier domain, followed by a shrinkage in the wavelet domain. The Fourier shrinkage is performed in a decorrelated space, by performing PCA before the Fourier transform. The wavelet shrinkage is performed in a Bayesian denoising framework by applying multicomponent probability density models for the wavelet coefficients that fully account for the intercomponent correlations. In an experimental section, we compare this procedure with the single-component analogies, i.e. performing the Fourier shrinkage in the correlated space and using single-component probability density models for the wavelet coefficients. In this way, the effect of the multicomponent procedures on the deconvolution and denoising performance is studied experimentally.
Year
DOI
Venue
2007
10.1007/978-3-540-74260-9_6
ICIAR
Keywords
Field
DocType
denoising step,bayesian denoising framework,fourier domain,denoising performance,fourier shrinkage,multicomponent image restoration,experimental study,wavelet domain,multicomponent image,wavelet coefficient,multicomponent probability density model,wavelet shrinkage,fourier transform,probability density,wavelet transform,image restoration
Harmonic wavelet transform,Pattern recognition,Computer science,Deconvolution,Second-generation wavelet transform,Discrete wavelet transform,Artificial intelligence,Stationary wavelet transform,Wavelet packet decomposition,Wavelet transform,Wavelet
Conference
Volume
ISSN
ISBN
4633
0302-9743
3-540-74258-1
Citations 
PageRank 
References 
0
0.34
10
Authors
3
Name
Order
Citations
PageRank
Arno Duijster1132.48
Steve De Backer220015.14
Paul Scheunders31190102.87