30 October 1997 Model-based Tikhonov regularization and performance for a shift-varying degradation
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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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Valerie Barakat, B. Guilpart, Robert Goutte, 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); doi: 10.1117/12.279556; https://doi.org/10.1117/12.279556
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