Restoration of images with nonstationary mean and autocorrelation

Author

Hillery, Allen D. ; Chin, Roland T.

Author_Institution

Dept. of Electr. Comput. Eng., Wisconsin Univ., Madison, WI, USA

fYear

1988

fDate

11-14 Apr 1988

Firstpage

1008

Abstract

Methods are investigated for the restoration of images degraded by both blur and noise. The objective is to develop estimation strategies to deal with images that exhibit spatially varying statistics. The restoration starts with transforming the image with nonstationary statistics into an image that exhibits stationary characteristics. This transformation can be viewed as a prewhitening filter that normalizes the local mean and local variance of the image, creating a stationary, or near stationary, field. Then the ideal image is estimated from the transformed image on the basis of the linear minimum-mean-square-error criterion. The process removes image blur and noise and at the same time inverts the effects of the transformation

Keywords

correlation methods; errors; estimation theory; filtering and prediction theory; noise; picture processing; autocorrelation; degraded image; estimation strategies; ideal image; image blur; image noise; image restoration; linear minimum-mean-square-error criterion; local mean; local variance; nonstationary mean; nonstationary statistics; prewhitening filter; spatially varying statistics; stationary characteristics; transformed image; Additive white noise; Autocorrelation; Covariance matrix; Degradation; Filters; Image restoration; Mean square error methods; Statistics; Vectors; White noise;

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.1988.196763

Filename

196763