Semantic-Driven Face Hallucination Based on Residual Network
Xiaoyuan Yu, Langwen Zhang, Wei Xie
Abstract
State-of-the-art face hallucination methods utilize convolutional neural networks to restore high-quality high-resolution (HR) face images from low-resolution (LR) face images by employing appearance knowledge. However, most of these solutions only deploy the appearance knowledge (such as face attributes) to modulate the first layer of convolution feature with channel-wise shifting and ignore the face-identity information while emphasizing the vision quality of the generated result. To address the above issues, we propose a semantic-driven residual network based on a generative adversarial network to restore the HR face image with proper identity from the LR face image. Firstly, precise semantic information is explored based on improved U-net. Secondly, the semantic information is concatenated into the residual blocks of the reconstruction module, which is exceptionally efficient in modulating the extracted feature and guiding the generation of HR face images. Finally, the proposed methods can obtain the pleasuring SR face image with a similar identity of LR face image, after adversarially optimizing the designed loss functions. Extensive experiments demonstrate that the proposed semantic-driven residual network performs against the state-of-the-arts both quantitatively and qualitatively.