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Proceedings Paper

General method for accelerating simulated annealing algorithms for Bayesian image restoration
Author(s): Griff L. Bilbro
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Paper Abstract

A new stochastic technique is described for the Bayesian restoration of gray-level images corrupted by white noise. The proposed technique is related to simulated annealing but generates candidates more efficiently for gray-level images than either the Gibbs sampler or the Metropolis procedure. For a logarithmic cooling schedule, asymptotic convergence of the algorithm is proved by analyzing the corresponding inhomogeneous Markov chain. For an exponential cooling schedule, the new technique is shown experimentally to restore floating point images in 1/50 of the time required for the usual simulated annealing. Experimental restorations of gray-level images corrupted by white noise are presented.

Paper Details

Date Published: 1 October 1991
PDF: 11 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48369
Show Author Affiliations
Griff L. Bilbro, North Carolina State Univ. (United States)


Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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