Simple shape parameter estimation from blurred observations for a generalized Gaussian MRF image prior used in MAP image restoration

Author

Jeffs, Brian D. ; Pun, Wai Ho

Author_Institution

Dept. of Electr. & Comput. Eng., Brigham Young Univ., Provo, UT, USA

Volume

1

fYear

1996

fDate

16-19 Sep 1996

Firstpage

465

Abstract

The generalized Gaussian Markov random field (GGMRF) is used as an image prior model in MAP restoration of blurred and noise corrupted images. This model is adapted to the characteristics of the true image by jointly estimating the true image and the GGMRF shape parameter, p, from the corrupted observation. A simple estimator for p based on sample kurtosis is introduced. It is shown that the value of p ranges widely when modeling typical images and texture fields. Higher quality restorations can be obtained when the estimated p value is used, rather than commonly used arbitrary choices

Keywords

Gaussian processes; Markov processes; image restoration; image sampling; image texture; maximum likelihood estimation; noise; random processes; GGMRF shape parameter; MAP image restoration; blurred image; blurred observations; corrupted observation; generalized Gaussian MRF image prior; image modeling; noise corrupted image; sample kurtosis; shape parameter estimation; texture fields; true image estimation; Bayesian methods; Degradation; Electronic mail; Gaussian noise; Image restoration; Markov random fields; Noise shaping; Parameter estimation; Shape; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1996. Proceedings., International Conference on

Conference_Location

Lausanne

Print_ISBN

0-7803-3259-8

Type

conf

DOI

10.1109/ICIP.1996.560887

Filename

560887