Share Email Print

Proceedings Paper

Wavelet-based multicomponent image restoration
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 2 October 2007
PDF: 10 pages
Proc. SPIE 6763, Wavelet Applications in Industrial Processing V, 67630J (2 October 2007); doi: 10.1117/12.733826
Show Author Affiliations
Arno Duijster, Univ. Antwerpen (Belgium)
Steve De Backer, Univ. Antwerpen (Belgium)
Paul Scheunders, Univ. Antwerpen (Belgium)

Published in SPIE Proceedings Vol. 6763:
Wavelet Applications in Industrial Processing V
Frédéric Truchetet; Olivier Laligant, Editor(s)

© SPIE. Terms of Use
Back to Top