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

Sean Borman; Ken D. Sauer; Charles A. Bouman

doi:10.1117/12.323816

22 September 1998 Nonlinear prediction methods for estimation of clique weighting parameters in non-Gaussian image models

Sean Borman, Ken D. Sauer, Charles A. Bouman

Author Affiliations +

Proceedings Volume 3459, Bayesian Inference for Inverse Problems; (1998) https://doi.org/10.1117/12.323816
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

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.

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Sean Borman, Ken D. Sauer, and Charles A. Bouman "Nonlinear prediction methods for estimation of clique weighting parameters in non-Gaussian image models", Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); https://doi.org/10.1117/12.323816

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