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In this paper, we present a Bayesian-based super-resolution algorithm that uses approximations of symmetric alpha-stable (\(S\alpha S\) ) Markov random fields as prior. The approximated \(S\alpha S\) prior is used to perform maximum a posteriori (MAP) estimation for the high-resolution (HR) image reconstruction process. Compared with other state-of-the-art prior models, the proposed prior can better...
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