Multicomponent image restoration, an experimental study

Authors:
Arno Duijster;Steve De Backer;Paul Scheunders
Affiliations:
IBBT, Vision Lab, University of Antwerp, Wilrijk, Belgium;IBBT, Vision Lab, University of Antwerp, Wilrijk, Belgium;IBBT, Vision Lab, University of Antwerp, Wilrijk, Belgium
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

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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.