<title>Hierarchical Bayesian approach to image restoration and the iterative evaluation of the regularization parameter</title>
Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIEPublished 16 September 1994
Rafael Molina, Aggelos K. Katsaggelos
Citations6
SJR score0.15
SNIP0.20
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TL;DR
This work relates the hierarchical Bayesian approach to image restoration to an iterative approach for estimating hyperparameters in a deterministic way.
Abstract
In an image restoration problem we usually have two different kinds of information. In the first stage, we have knowledge about the structural form of the noise and local characteristics of the image. These noise and image models normally depend on unknown hyperparameters. The hierarchical Bayesian approach adds a second stage by putting a hyperprior on these hyperparameters, through which information about these hyperparameters is included. In this work we relate the hierarchical Bayesian approach to image restoration to an iterative approach for estimating these hyperparameters in a deterministic way.
Keywords
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