Hierarchical Bayesian approach to image restoration and the iterative evaluation of the regularization parameter

Rafael Molina*, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Pages244-251
Number of pages8
Volume2308
Editionp 1
StatePublished - Dec 1 1994
EventVisual Communications and Image Processing '94 - Chicago, IL, USA
Duration: Sep 25 1994Sep 29 1994

Other

OtherVisual Communications and Image Processing '94
CityChicago, IL, USA
Period9/25/949/29/94

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

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