Title :
Simple shape parameter estimation from blurred observations for a generalized Gaussian MRF image prior used in MAP image restoration
Author :
Jeffs, Brian D. ; Pun, Wai Ho
Author_Institution :
Dept. of Electr. & Comput. Eng., Brigham Young Univ., Provo, UT, USA
Abstract :
The generalized Gaussian Markov random field (GGMRF) is used as an image prior model in MAP restoration of blurred and noise corrupted images. This model is adapted to the characteristics of the true image by jointly estimating the true image and the GGMRF shape parameter, p, from the corrupted observation. A simple estimator for p based on sample kurtosis is introduced. It is shown that the value of p ranges widely when modeling typical images and texture fields. Higher quality restorations can be obtained when the estimated p value is used, rather than commonly used arbitrary choices
Keywords :
Gaussian processes; Markov processes; image restoration; image sampling; image texture; maximum likelihood estimation; noise; random processes; GGMRF shape parameter; MAP image restoration; blurred image; blurred observations; corrupted observation; generalized Gaussian MRF image prior; image modeling; noise corrupted image; sample kurtosis; shape parameter estimation; texture fields; true image estimation; Bayesian methods; Degradation; Electronic mail; Gaussian noise; Image restoration; Markov random fields; Noise shaping; Parameter estimation; Shape; Vectors;
Conference_Titel :
Image Processing, 1996. Proceedings., International Conference on
Conference_Location :
Lausanne
Print_ISBN :
0-7803-3259-8
DOI :
10.1109/ICIP.1996.560887
