Face super-resolution (SR) has become an indispensable function in security
solutions such as video surveillance and identification system, but the
distortion in facial components is a great challenge in it. Most
state-of-the-art methods have utilized facial priors with deep neural networks.
These methods require extra labels, longer training time, and larger
computation memory. In this paper, we propose a novel Edge and Identity
Preserving Network for Face SR Network, named as EIPNet, to minimize the
distortion by utilizing a lightweight edge block and identity information. We
present an edge block to extract perceptual edge information, and concatenate
it to the original feature maps in multiple scales. This structure
progressively provides edge information in reconstruction to aggregate local
and global structural information. Moreover, we define an identity loss
function to preserve identification of SR images. The identity loss function
compares feature distributions between SR images and their ground truth to
recover identities in SR images. In addition, we provide a
luminance-chrominance error (LCE) to separately infer brightness and color
information in SR images. The LCE method not only reduces the dependency of
color information by dividing brightness and color components but also enables
our network to reflect differences between SR images and their ground truth in
two color spaces of RGB and YUV. The proposed method facilitates the proposed
SR network to elaborately restore facial components and generate high quality
8x scaled SR images with a lightweight network structure. Furthermore, our
network is able to reconstruct an 128x128 SR image with 215 fps on a GTX 1080Ti
GPU. Extensive experiments demonstrate that our network qualitatively and
quantitatively outperforms state-of-the-art methods on two challenging
datasets: CelebA and VGGFace2.

该论文提出了一种有效的人脸超分辨率恢复方法，称为 EIPNet，采用轻量级边缘块和身份信息来最小化变形。该方法还定义了身份损失函数来保留 SR 图像的身份信息，提供了 LCE 方法来分别推断 SR 图像中的亮度和颜色信息，并使网络能够在 RGB 和 YUV 两个颜色空间中反映 SR 图像与它们的 Ground Truth 之间的差异。大量实验证明，该网络在两个具有挑战性的数据集 CelebA 和 VGGFace2 上表现优于最先进的方法。