Model-based Tikhonov regularization and performances for a shift-varying degradation

Valerie Barakat; B. Guilpart; Robert Goutte; Remy Prost

doi:10.1117/12.279556

30 October 1997 Model-based Tikhonov regularization and performance for a shift-varying degradation

Valerie Barakat, B. Guilpart, Robert Goutte, Remy Prost

Proceedings Volume 3164, Applications of Digital Image Processing XX; (1997) https://doi.org/10.1117/12.279556
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

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.

Citation Download Citation

Valerie Barakat, B. Guilpart, Robert Goutte, and Remy Prost "Model-based Tikhonov regularization and performance for a shift-varying degradation", Proc. SPIE 3164, Applications of Digital Image Processing XX, (30 October 1997); https://doi.org/10.1117/12.279556

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