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Proceedings Paper

Hierarchical Bayesian approach to image restoration and the iterative evaluation of the regularization parameter
Author(s): Rafael Molina; Aggelos K. Katsaggelos
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Paper 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.

Paper Details

Date Published: 16 September 1994
PDF: 8 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.185966
Show Author Affiliations
Rafael Molina, Univ. de Granada (Spain)
Aggelos K. Katsaggelos, Northwestern Univ. (United States)


Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)

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