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Proceedings Paper

Model-based Tikhonov regularization and performances for a shift-varying degradation
Author(s): Valerie Barakat; B. Guilpart; Robert Goutte; Remy Prost
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Paper Abstract

The purpose of this report is to propose a new restoration technique, based on the Tikhonov regularization approach, including local properties about the original image into the restoration process, with the use of an a priori model of the solution. In order to prove the effectiveness of the proposal, we compare it with three restoration methods of images: usual Tikhonov regularization, Markov-fields and maximum entropy. In image restoration, the problem is usually addressed under the assumption that the blur operation is shift-invariant. Since real- world blurs are often shift-variant, we will either consider the shift-variant problem and its approximation, or we will use a simplifying approximation, by an invariance blur. A criteria will be defined to validate, in terms of quality restoration, the approximation of a spatially- variant blur by an invariant one. Simulation results show that the proposed method, with an accurate a priori model, out-performs the conventional Tikhonov regularization. The influence of the space-variability will be illustrated on images.

Paper Details

Date Published: 30 October 1997
PDF: 11 pages
Proc. SPIE 3164, Applications of Digital Image Processing XX, (30 October 1997); doi: 10.1117/12.279556
Show Author Affiliations
Valerie Barakat, CEA/Bruyeres-le-Chatel (France)
B. Guilpart, CEA/Bruyeres-le-Chatel (France)
Robert Goutte, Institut National des Sciences Appliquees de Lyon (France)
Remy Prost, Institut National des Sciences Appliquees de Lyon (France)

Published in SPIE Proceedings Vol. 3164:
Applications of Digital Image Processing XX
Andrew G. Tescher, Editor(s)

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