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

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

Oct 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997spie.3164..210b&link_type=abstract

Proc. SPIE Vol. 3164, p. 210-220, Applications of Digital Image Processing XX, Andrew G. Tescher; Ed.

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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.

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