Title :
A generalized Gauss Markov model for space objects in blind restoration of adaptive optics telescope images
Author :
Jeffs, Brian D. ; Hong, Sheila ; Christou, Julian
Author_Institution :
Dept. of Electr. & Comput. Eng., Brigham Young Univ., Provo, UT, USA
Abstract :
This paper introduces a blind method based on Bayesian maximum a posteriori estimation theory for restoring images corrupted by noise and blurred by one or more unknown point spread functions. Image and blur prior information is expressed in the form of parametric generalized Gauss Markov random field models. A method for estimating the GGMRF neighborhood influence parameters is presented, along with examples of blind restoration to reduce residual blur in adaptive optics telescope images of space objects
Keywords :
Bayes methods; Gaussian processes; Markov processes; adaptive optics; astronomical techniques; image enhancement; image restoration; maximum likelihood estimation; optical transfer function; parameter estimation; Bayesian maximum a posteriori estimation theory; GGMRF neighborhood influence parameters; adaptive optics telescope images; blind restoration; blur; generalized Gauss Markov model; noise; parametric generalized Gauss Markov random field models; prior information; space objects; unknown point spread functions; Adaptive optics; Bayesian methods; Gaussian processes; Image restoration; Markov random fields; Maximum a posteriori estimation; Optical noise; Pixel; Probability density function; Shape; Telescopes;
Conference_Titel :
Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
0-8186-8821-1
DOI :
10.1109/ICIP.1998.727363
