A new look at maximum entropy image reconstruction

Author

Willis, Matthew ; Jeffs, Brian D. ; Long, David G.

Author_Institution

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

Volume

2

fYear

1999

fDate

24-27 Oct. 1999

Firstpage

1272

Abstract

This paper presents new insights into the maximum entropy (ME) method of image restoration. It is shown that when a specific image prior probability PDF model is chosen for Bayesian MAP restoration, the resulting solution is identical to the maximum entropy result. This relationship provides a new means of evaluating the theoretical foundations of maximum entropy and may assist in determining what class of images are best suited for ME processing. Also, a new non-iterative, closed-form approximation to the ME solution is developed. This result can reduce computational demands compared to conventional iterative algorithms. An example of the closed form restoration is presented.

Keywords

Bayes methods; approximation theory; image restoration; maximum entropy methods; optimisation; probability; Bayesian MAP restoration; PDF model; closed form restoration; computational demands reduction; image class; iterative algorithms; maximum entropy image reconstruction; noniterative closed-form approximation; unconstrained optimization problem; Additive noise; Bayesian methods; Constraint optimization; Constraint theory; Entropy; Image reconstruction; Image restoration; Least squares methods; Quantum mechanics; Radio astronomy;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems, and Computers, 1999. Conference Record of the Thirty-Third Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-7803-5700-0

Type

conf

DOI

10.1109/ACSSC.1999.831911

Filename

831911