Deterministic parallel computation of Bayesian deblurring using cluster approximations

Author

Wu, Chi-hsin ; Doerschuk, Peter C.

Author_Institution

Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA

fYear

1993

fDate

3-6 May 1993

Firstpage

395

Abstract

A family of approximations to Bayesian estimators based on Markov random fields models of images and mean squared error reconstruction criteria is described. The computation of the estimator requires the solution of a multivariable fixed point problem for which existence, uniqueness, and convergent algorithm results are stated. These algorithms preserve the structure of the grey levels. Two simple examples are given which show excellent performance

Keywords

Bayes methods; Markov processes; approximation theory; estimation theory; image enhancement; image restoration; parallel algorithms; Bayesian deblurring; Bayesian estimators; Markov random fields models; cluster approximations; convergent algorithm; deterministic parallel computation; grey levels; image processing; mean squared error reconstruction criteria; multivariable fixed point problem; Bayesian methods; Computational complexity; Computational modeling; Concurrent computing; Cost function; Image processing; Lattices; Markov random fields; Random variables; Temperature distribution;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1993., ISCAS '93, 1993 IEEE International Symposium on

Conference_Location

Chicago, IL

Print_ISBN

0-7803-1281-3

Type

conf

DOI

10.1109/ISCAS.1993.393741

Filename

393741