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Restoration of noisy images modeled by Markov random fields with Gibbs distribution

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3 Author(s)
Shridhar, M. ; Dept. of Electr. & Comput. Eng., Michigan Univ., Dearborn, MI, USA ; Ahmadi, M. ; El-Gabali, M.

The authors develop techniques for restoration of noisy images using Markov/Gibbs random fields. In the schemes to be presented, the local characteristics of the noise-free image are described by pairwise-interaction Markov random fields, while the noise, assumed to be mainly additive, is modeled as a zero-mean Gaussian process. The estimation of the clean image is based on the MAP criterion. Optimal estimates are derived with proper choice of performance criteria. Studies undertaken with a variety of images have confirmed the feasibility of the proposed techniques under conditions of high noise

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Circuits and Systems, IEEE Transactions on  (Volume:36 ,  Issue: 6 )