Face Hallucination Using Manifold-Regularized Group Locality-Constrained Representation

Sparsity and locality regularizations are successfully applied to face hallucination algorithms to ameliorate their ill-posed nature. However, most of patch-based face hallucination approaches only consider the manifold structure of single patch, thus resulting in unstable solution for image reconstruction. In this paper, we propose a novel face hallucination, termed manifold-regularized group locality-constrained representation (MGLR), in order to exploit the multiple manifold structures rooted in grouped self-similarly patches. Specifically, we first group similar patches to form a matrix which contains the recurrent non-local patches. Then graph regularization term is formulated to represent the group manifolds for better reconstruction quality. Taking advantages of grouped self-similar patches, MGLR can offer stable sparse solution to take advantage of the the accurate prior for super-resolution reconstruction. Experimental results on LFW database and CMU real-world images demonstrate the superiority of the proposed method over some state-of-the-art face methods both in terms of subjective and objective qualities.