Face hallucination through differential evolution parameter map learning with facial structure prior

Current learning based face hallucination approaches mainly focus on how to design a reasonable objective function, such as using different assumptions and incorporating different regularization terms, but do not give a reasonable way of selecting the model parameters. In this paper, we propose to exploit the facial structure prior to learn a parameter map based on differential evolution. Specifically, we claim that different position patches have different parameter settings because of their different statistical properties, and patches from the same position of different face images should have similar parameter settings. As a result, we first learn a parameter map for each training sample by leveraging an evolutionary algorithm based on differential evolution, and then fuse these learned parameter maps to an optimal parameter map for testing via mean-pooling strategy. Finally, we use the predicted parameter map to guide the co-occurrence relationship modeling in different regions of the input low-resolution (LR) face image. Experimental results demonstrate that, even without seeing the ground truth, results of proposed parameter map learning method are comparable to or better than those traditional unified parameter setting methods and some recently proposed deep learning methods. (C) 2018 Elsevier Inc. All rights reserved.