Efficient learning based face hallucination approach via facial standard deviation prior

Most state-of-the-art face hallucination approaches suffer from complicated learning patterns and highly intensive computation, which will lead to low efficiency and considerable computing resources. Therefore, how to restore real face image quickly and efficiently is still an important issue in this field. To solve or partially solve the problem, this paper proposed a novel facial standard deviation prior based approach which can provide superior results with high efficiency for real face images. The high frequency information of test image will be enhanced via a facial specific sharpening operator which is obtained through the learning of standard deviation correspondence of training set. Experiments in simulation and real world images verified the effectiveness of proposed approach, and the distinct advantage on runtime and resource requirement of proposed approach.