Adaptive image restoration using a generalized Gaussian model for unknown noise

Author

Pun, Wai Ho ; Jeffs, Brian D.

Author_Institution

Brigham Young Univ., Provo, UT, USA

Volume

4

Issue

10

fYear

1995

fDate

10/1/1995 12:00:00 AM

Firstpage

1451

Lastpage

1456

Abstract

A model adaptive method is proposed for restoring blurred and noise corrupted images. The generalized p-Gaussian family of probability density functions is used as the approximating parametric noise model. Distribution shape parameters are estimated from the image, and the resulting maximum likelihood optimization problem is solved. An iterative algorithm for data-directed restoration is presented and analyzed

Keywords

Gaussian distribution; Gaussian processes; adaptive signal processing; image restoration; iterative methods; maximum likelihood estimation; noise; optimisation; adaptive image restoration; approximation; blurred images; data-directed restoration; distribution shape parameters; generalized Gaussian model; iterative algorithm; maximum likelihood optimization problem; noise corrupted images; parameter estimation; parametric noise model; probability density functions; unknown noise; Degradation; Gaussian noise; Image edge detection; Image restoration; Iterative algorithms; Least squares methods; Maximum likelihood estimation; Noise shaping; Shape; Vectors;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.465110

Filename

465110