Title :
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
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;
Conference_Titel :
Signals, Systems, and Computers, 1999. Conference Record of the Thirty-Third Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-7803-5700-0
DOI :
10.1109/ACSSC.1999.831911
