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Proceedings Paper

Nonlinear prediction methods for estimation of clique weighting parameters in non-Gaussian image models
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Paper Abstract

NonGaussian Markov image models are effective in the preservation of edge detail in Bayesian formulations of restoration and reconstruction problems. Included in these models are coefficients quantifying the statistical links among pixels in local cliques, which are typically assumed to have an inverse dependence on distance among the corresponding neighboring pixels. Estimation of these coefficients is a nontrivial task for Non Gaussian models. We present rules for coefficient estimation for edge- preserving models which are particularly effective for edge preservation and noise suppression, using a predictive technique analogous to estimation of the weights of optimal weighted median filters.

Paper Details

Date Published: 22 September 1998
PDF: 9 pages
Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); doi: 10.1117/12.323816
Show Author Affiliations
Sean Borman, Univ. of Notre Dame (United States)
Ken D. Sauer, Univ. of Notre Dame (United States)
Charles A. Bouman, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 3459:
Bayesian Inference for Inverse Problems
Ali Mohammad-Djafari, Editor(s)

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