Restoration of images degraded by compound noise sources using Markov random field models

Authors:
M. Shridhar;M. Ahmadi;M. El-Gabali
Affiliations:
Department of Electrical Engineering, University of Windsor, Windsor, Ontario, Canada N9B 3P4;Department of Electrical Engineering, University of Windsor, Windsor, Ontario, Canada N9B 3P4;Department of Mathematics and Computer Science, Faculty of Science, University of Kuwait, P. O. Box 5969, Kuwait
Venue:
Journal of Visual Communication and Image Representation
Year:
1992

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Abstract

The main theme of this paper is the derivation of a new algorithm for restoring digitized images degraded by both additive and multiplicative noise sources. In order to keep the derivation sufficiently general, the authors also include degradation caused by blur and a class of nonlinearities. The images under consideration are modeled as Markov random fields, while the additive and multiplicative noise sources are assumed to be Gaussian processes with known means and variances. Blurring of images is accomplished by a shift-invariant point-spread function. Test results with degraded images indicate that the algorithm is effective in restoring images degraded by high levels of additive and multiplicative noise.