Progressive Semantic-Aware Style Transformation for Blind Face Restoration

• URL: http://arxiv.org/abs/2009.08709v2
• Date: Sun, 21 Mar 2021 09:35:05 GMT
• Title: Progressive Semantic-Aware Style Transformation for Blind Face Restoration
• Authors: Chaofeng Chen, Xiaoming Li, Lingbo Yang, Xianhui Lin, Lei Zhang, Kwan-Yee K. Wong
• Abstract summary: We propose a new progressive semantic-aware style transformation framework, named PSFR-GAN, for face restoration. The proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of input pairs. 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 better to generalize natural LQ face images.
• Score: 26.66332852514812
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 https://github.com/chaofengc/PSFRGAN.

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