Blind face restoration (BFR) from severely degraded face images in the wild
is a very challenging problem. Due to the high illness of the problem and the
complex unknown degradation, directly training a deep neural network (DNN)
usually cannot lead to acceptable results. Existing generative adversarial
network (GAN) based methods can produce better results but tend to generate
over-smoothed restorations. In this work, we propose a new method by first
learning a GAN for high-quality face image generation and embedding it into a
U-shaped DNN as a prior decoder, then fine-tuning the GAN prior embedded DNN
with a set of synthesized low-quality face images. The GAN blocks are designed
to ensure that the latent code and noise input to the GAN can be respectively
generated from the deep and shallow features of the DNN, controlling the global
face structure, local face details and background of the reconstructed image.
The proposed GAN prior embedded network (GPEN) is easy-to-implement, and it can
generate visually photo-realistic results. Our experiments demonstrated that
the proposed GPEN achieves significantly superior results to state-of-the-art
BFR methods both quantitatively and qualitatively, especially for the
restoration of severely degraded face images in the wild. The source code and
models can be found at this https URL

该研究提出了一种将基于生成对抗网络的高质量人脸图像生成器嵌入到 U 型深度神经网络中的盲目人脸修复方法，其能更好地控制全局脸部结构、局部面部细节和背景，并在野外环境下显著提高盲目人脸修复的效果。