Approximate alpha-stable Markov Random Fields for video super-resolution

Jin Chen, Jose Nunez-Yanez, Alin Achim

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)


In the paper, we present a Bayesian super resolution method that uses an approximation of symmetric alpha-stable (SaS) Markov Random Fields as prior. The approximated SaS prior is employed to perform a 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. Since the corresponding energy function is non-convex, the iterated conditional modes (ICM) method is used to solve the MAP estimation. Results indicate a significant improvement over other super resolution algorithms.
Original languageEnglish
Title of host publication2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO 2012)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Print)9781467310680
Publication statusPublished - Jan 2013
Event20th European Signal Processing Conference (EUSIPCO) - Bucharest, Romania
Duration: 27 Aug 201231 Aug 2012

Publication series

NameProceedings of the European Signal Processing Conference (EUSIPCO)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)2076-1465


Conference20th European Signal Processing Conference (EUSIPCO)


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