A Coarse-to-Fine Face Hallucination Method by Exploiting Facial Prior Knowledge

Face hallucination technique generates high-resolution (HR) face images from low-resolution (LR) ones. In this paper, we propose to use a coarse-to-fine method for face hallucination by constructing a two-branch network, which makes full use of the specific prior knowledge of face images and the advantages of generic image super-resolution (SR) methods. Specifically, we jointly build a deep neural network (DNN) with a face image SR branch and a semantic face parsing branch. The former branch implements the image upsampling and feature extraction using a cascade of convolutional layers. The latter branch extracts facial semantic parsing as prior knowledge. Then, we combine the image features and the prior know ledge to reconstruct HR face images. Finally, we optimize the DNN, by using adversarial training and a perceptual loss, in order to obtain high realism. Extensive experiments show that the proposed method outperforms the state-of-the-art alternatives in terms of accuracy and realism.