Bayesian video super-resolution with heavy-tailed prior models

Jin Chen, Jose L. Nunez-Yanez, Alin Achim

Research output: Contribution to journalArticle (Academic Journal)peer-review

11 Citations (Scopus)


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 capture the heavy tails of the distribution of the HR image. Thus, the edges of the reconstructed HR image are preserved better in our method. As the corresponding energy function is nonconvex, the graduated nonconvexity method is used to solve the MAP estimation. Experiments confirm the better fit achieved by the proposed model to the actual data distribution and the consequent improvement in terms of visual quality over previously proposed super-resolution algorithms.

Original languageEnglish
Article number6732896
Pages (from-to)905-914
Number of pages10
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number6
Publication statusPublished - 1 Jan 2014


  • Bayesian super-resolution
  • heavy-tailed Markov random field.


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