Image estimation by stochastic relaxation in the compound Gaussian case

Author

Jeng, Fure-Ching ; Woods, John W.

Author_Institution

Rensselaer Polytech. Inst., Troy, NY, USA

fYear

1988

fDate

11-14 Apr 1988

Firstpage

1016

Abstract

Concerns developing algorithms for obtaining the maximum a posteriori probability (MAP) estimate from blurred and noisy images modeled as compound Gauss-Markov random fields. These models consist of several image submodels having different characteristics along with a structure model, a 2D Markov chain, which governs transitions between these image submodels. Compound random field models are attractive for image estimation because the resulting estimates do not suffer the over-smoothing of edges that occurs when one employs linear shift-invariant (LSI) models

Keywords

Markov processes; estimation theory; picture processing; random processes; stochastic processes; 2D Markov chain; MAP; blurred images; compound Gauss-Markov random fields; compound Gaussian case; edges; image estimation; maximum a posteriori probability; noisy images; over-smoothing; stochastic relaxation; Bonding; Computer aided software engineering; Gaussian processes; Humans; Image restoration; Large scale integration; Markov random fields; Nonlinear filters; Stochastic processes; User-generated content;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on

Conference_Location

New York, NY

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1988.196765

Filename

196765