Abstract
In the paper, we present a Bayesian super resolution method that uses an approximation of symmetric alpha-stable (SαS) Markov Random Fields as prior. The approximated SαS 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.
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