Face super-resolution using 8-connected Markov Random Fields with embedded prior

In patch based face super-resolution method, the patch size is usually very small, and neighbor patchespsila relationship via overlapped regions is only to keep smoothness of reconstructed high-resolution image, so the prior is not always strong enough to regularize super-resolution when observed low-resolution image lose facial structure information. We propose to use Gaussian Mixture Model(GMM) to learn facial prior embedded between un-overlapped regions of neighbor patches. This approach, which has never been used to regularize face super-resolution before, usually works as a potential function in 8-connected Markov Random Fields (MRFs) with belief propagation. In the proposed algorithm, we assign high probability to the neighbor candidate patches that express correct facial structure, and others not. Experiments demonstrate that our method is superior in preserving smoothness and recovers facial structure and local details when low-resolution image lost the details of facial structure.