Face restoration is important in face image processing, and has been widely
studied in recent years. However, previous works often fail to generate
plausible high quality (HQ) results for real-world low quality (LQ) face
images. In this paper, we propose a new progressive semantic-aware style
transformation framework, named PSFR-GAN, for face restoration. Specifically,
instead of using an encoder-decoder framework as previous methods, we formulate
the restoration of LQ face images as a multi-scale progressive restoration
procedure through semantic-aware style transformation. Given a pair of LQ face
image and its corresponding parsing map, we first generate a multi-scale
pyramid of the inputs, and then progressively modulate different scale features
from coarse-to-fine in a semantic-aware style transfer way. Compared with
previous networks, the proposed PSFR-GAN makes full use of the semantic
(parsing maps) and pixel (LQ images) space information from different scales of
input pairs. In addition, we further introduce a semantic aware style loss
which calculates the feature style loss for each semantic region individually
to improve the details of face textures. Finally, we pretrain a face parsing
network which can generate decent parsing maps from real-world LQ face images.
Experiment results show that our model trained with synthetic data can not only
produce more realistic high-resolution results for synthetic LQ inputs and but
also generalize better to natural LQ face images compared with state-of-the-art
methods. Codes are available at this https URL

本文提出了一种名为 PSFR-GAN 的新型渐进式语义感知风格转换框架，用于面部图像修复。该框架通过多尺度逐步修复 LQ 面部图像，并在语义感知风格转移的同时从粗糙到精细地调制不同比例尺寸的特征。同时，引入了一种语义感知风格损失，以改善面部纹理的细节，并通过合成数据进行了实验验证。