General method for accelerating simulated annealing algorithms for Bayesian image restoration

Griff L. Bilbro

doi:10.1117/12.48369

1 October 1991 General method for accelerating simulated annealing algorithms for Bayesian image restoration

Griff L. Bilbro

Proceedings Volume 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision; (1991) https://doi.org/10.1117/12.48369
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

Citation Download Citation

Griff L. Bilbro "General method for accelerating simulated annealing algorithms for Bayesian image restoration", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48369

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