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Proceedings Paper

Approach to regularization preconditioners for image processing
Author(s): Claudio Estatico
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Paper Abstract

Preconditioning techniques for linear systems are widely used in order to speed up the convergence of iterative methods. Unfortunately, linear systems arising in image processing are highly ill-conditioned and preconditioners often give bad results, since the noise components on the data are strongly amplified already at the early iterations. In this work, we propose filtering strategies which allow to obtain preconditioners with rgularization features for Toeplitz systems of image deblurring. Regularization preconditioners are able to speed up the convergence in the space less sensitive to the noise and, simultaneously, they slow down the restoration from components mainly corrupted by noise. A 2-d numerical simulation concerning astronomical image deblurring confirms the effectiveness of the arguments.

Paper Details

Date Published: 24 December 2003
PDF: 12 pages
Proc. SPIE 5205, Advanced Signal Processing Algorithms, Architectures, and Implementations XIII, (24 December 2003); doi: 10.1117/12.504482
Show Author Affiliations
Claudio Estatico, Univ. degli Studi di Genova (Italy)


Published in SPIE Proceedings Vol. 5205:
Advanced Signal Processing Algorithms, Architectures, and Implementations XIII
Franklin T. Luk, Editor(s)

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