Edge and Identity Preserving Network for Face Super-Resolution

Face super-resolution has become an indispensable part in security problems such as video surveillance and identification system, but the distortion in facial components is a main obstacle to overcoming the problems. To alleviate it, most stateof-the-arts have utilized facial priors by using deep networks. These methods require extra labels, longer training time, and larger computation memory. Thus, we propose a novel Edge and Identity Preserving Network for Face Super-Resolution Network, named as EIPNet, which minimizes the distortion by utilizing a lightweight edge block and identity information. Specifically, the edge block extracts perceptual edge information and concatenates it to original feature maps in multiple scales. This structure progressively provides edge information in reconstruction procedure to aggregate local and global structural information. Moreover, we define an identity loss function to preserve identification of super-resolved images. The identity loss function compares feature distributions between super-resolved images and target images to solve unlabeled classification problem. In addition, we propose a Luminance-Chrominance Error (LCE) to expand usage of image representation domain. The LCE method not only reduces the dependency of color information by dividing brightness and color components but also facilitates our network to reflect differences between Super-Resolution (SR) and High- Resolution (HR) images in multiple domains (RGB and YUV). The proposed methods facilitate our super-resolution network to elaborately restore facial components and generate enhanced 8x scaled super-resolution images with a lightweight network structure.