Compound Gauss-Markov random fields for astronomical image restoration

Over the last few years, a growing number of researchers from varied disciplines have been utilizing Markov random fields (MRF) models for developing optimal, robust algorithms for various problems, such as texture analysis, image synthesis, image restoration, classification and segmentation, surface reconstruction, integration of several low level vision modules and sensor fusion. While linear-shift invariant (LSI) models have been generally used for image restoration in astronomy, no much work has been reported on the use of more complex models in this area. In this paper we examine the use of Compound Gaussian Markov Random Fields, (CGMRF), a non LSI model that preserves image discontinuities, to restore astronomical images. Problems on the application of the model arising from the high dynamic range and severe blurring of astronomical images are addressed and two new methods to estimate the real underlying image are proposed. The methods are tested on real astronomical images.