Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105460
Title: | Progressive semantic-aware style transformation for blind face restoration | Authors: | Chen, C Li, X Yang, L Lin, X Zhang, L Wong, KYK |
Issue Date: | 2021 | Source: | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, 19-25 June 2021, p. 11891-11900 | 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 but also generalize better to natural LQ face images compared with state-of-the-art methods. | Publisher: | Institute of Electrical and Electronics Engineers | ISBN: | 978-1-6654-4509-2 (Electronic) 978-1-6654-4510-8 (Print on Demand(PoD)) |
DOI: | 10.1109/CVPR46437.2021.01172 | Rights: | ©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication C. Chen, X. Li, L. Yang, X. Lin, L. Zhang and K. -Y. K. Wong, "Progressive Semantic-Aware Style Transformation for Blind Face Restoration," 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021, pp. 11891-11900 is available at https://doi.org/10.1109/CVPR46437.2021.01172. |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Li_Progressive_Semantic-Aware_Style.pdf | Pre-Published version | 3.95 MB | Adobe PDF | View/Open |
Page views
12
Citations as of Jun 2, 2024
SCOPUSTM
Citations
85
Citations as of May 31, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.