Progressive Semantic-Aware Style Transformation for Blind Face Restoration

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 but also generalize better to natural LQ face images compared with state-of-the-art methods.

Automated summary

The paper proposes a new progressive semantic-aware style transformation framework, named PSFR-GAN, for face restoration, which can generate high-quality face images by utilizing semantic information and pixel information from different scales of input pairs. By introducing semantic aware style loss and pretraining a face parsing network, the model trained with synthetic data shows better performance in producing high-resolution results and generalizing to natural LQ face images compared to state-of-the-art methods.