  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 https://github.com/yangxy/GPEN.
