Paper
16 September 1994 Hierarchical Bayesian approach to image restoration and the iterative evaluation of the regularization parameter
Rafael Molina, Aggelos K. Katsaggelos
Author Affiliations +
Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.185966
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States
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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rafael Molina and Aggelos K. Katsaggelos "Hierarchical Bayesian approach to image restoration and the iterative evaluation of the regularization parameter", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.185966
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Cited by 6 scholarly publications.
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KEYWORDS
Image restoration

Autoregressive models

Synthetic aperture radar

Cameras

Deconvolution

Image analysis

Image processing

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