Stochastic modeling and estimation of multispectral image data

Author

Schultz, Richard R. ; Stevenson, Robert L.

Author_Institution

Dept. of Electr. Eng., Notre Dame Univ., IN, USA

Volume

v

fYear

1994

fDate

19-22 Apr 1994

Abstract

The application of a monochromatic restoration algorithm to each channel within a multispectral image does not result in an estimate which takes into account cross-channel correlation. A non-Gaussian prior model is proposed for multispectral images, using the Gibbs distribution. The density accounts for both spatial (within channel) and spectral (between channel) information. Spatial components use a nonlinear operator to preserve discontinuities within each channel, while spectral components incorporate cross-channel information in the model. The prior density is used in a maximum a posteriori (MAP) estimation algorithm for the restoration of color images. The resulting nonlinear estimates are shown to be quantitatively superior to linear estimates produced by multichannel Wiener and least squares restoration, which implicitly use Gaussian priors

Keywords

image restoration; maximum likelihood estimation; stochastic processes; Gibbs distribution; MAP estimation algorithm; color image restoration; cross-channel correlation; cross-channel information; density; estimation; monochromatic restoration algorithm; multispectral image data; nonGaussian prior model; nonlinear estimates; nonlinear operator; spatial information; spectral information; stochastic modeling; Color; Degradation; Frequency; Image analysis; Image restoration; Least squares methods; Multispectral imaging; Optical sensors; Stochastic processes; Wiener filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389410

Filename

389410