Face hallucination from low quality images using definition-scalable inference

Abstract

To hallucinate super-resolution (super-res) face from a real low-quality face, a super-resolution technique based on definition-scalable inference (SRDSI) is proposed in this paper. In the proposed strategy, all high-res labeled faces are first decomposed into basic faces and enhanced faces to train a basic face and an enhanced face inferring model, and then two inferring models are used to hallucinate super-res basic face with low-definition and enhanced faces with high-frequency information from a single low-res face. Finally, the basic face is merged with its enhanced face into a super-res face with high-definition. In addition, this paper employs SIFT key-points to evaluate the similarity between the super-res face and its high-res labeled face. Experimental results show that SRDSI can effectively recover more structural information as well as SIFT key-points from real low-res faces and achieves better performance than state-of-the-art super-resolution techniques in terms of both visual and objective quality.