By Topic

Bayesian Video Super-Resolution With Heavy-Tailed Prior Models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jin Chen ; Dept. of Electr. & Electron. Eng., Univ. of Bristol, Bristol, UK ; Nunez-Yanez, J.L. ; Achim, A.

In this paper, we present a Bayesian-based superresolution algorithm that uses approximations of symmetric alpha-stable (SαS) Markov random fields as prior. The approximated SαS 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.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:24 ,  Issue: 6 )