DEEP LEARNING FACE HALLUCINATION VIA ATTRIBUTES TRANSFER AND ENHANCEMENT

Face hallucination technique aims to generate high-resolution (HR) face images from low-resolution (LR) inputs. Even though existing face hallucination methods have achieved great performance on the global region evaluation, most of them cannot reasonably restore local attributes, especially when ultra-resolving tiny LR face image (16x16 pixels) to its larger version (8x upscaling factor). In this paper, we propose a novel attribute-guided face transfer and enhancement network for face hallucination. Specifically, we first construct a face transfer network, which upsamples LR face images to HR feature maps, and then fuses facial attributes and the upsampled features to generate HR face images with rational attributes. Finally, a face enhancement network is developed based on generative adversarial network (GAN) to improve visual quality by exploiting a composite loss that combines image color, texture and content. Extensive experiments demonstrate that our method achieves superior face hallucination results and outperforms the state-of-the-art.