Learning warped guidance for blind face restoration

Xiaoming Li, Ming Liu, Yuting Ye, Wangmeng Zuo, Liang Lin, Ruigang Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

This paper studies the problem of blind face restoration from an unconstrained blurry, noisy, low-resolution, or compressed image (i.e., degraded observation). For better recovery of fine facial details, we modify the problem setting by taking both the degraded observation and a high-quality guided image of the same identity as input to our guided face restoration network (GFRNet). However, the degraded observation and guided image generally are different in pose, illumination and expression, thereby making plain CNNs (e.g., U-Net) fail to recover fine and identity-aware facial details. To tackle this issue, our GFRNet model includes both a warping subnetwork (WarpNet) and a reconstruction subnetwork (RecNet). The WarpNet is introduced to predict flow field for warping the guided image to correct pose and expression (i.e., warped guidance), while the RecNet takes the degraded observation and warped guidance as input to produce the restoration result. Due to that the ground-truth flow field is unavailable, landmark loss together with total variation regularization are incorporated to guide the learning of WarpNet. Furthermore, to make the model applicable to blind restoration, our GFRNet is trained on the synthetic data with versatile settings on blur kernel, noise level, downsampling scale factor, and JPEG quality factor. Experiments show that our GFRNet not only performs favorably against the state-of-the-art image and face restoration methods, but also generates visually photo-realistic results on real degraded facial images.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
EditorsVittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert
Pages278-296
Number of pages19
DOIs
StatePublished - 2018
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: Sep 8 2018Sep 14 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11217 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th European Conference on Computer Vision, ECCV 2018
Country/TerritoryGermany
CityMunich
Period9/8/189/14/18

Bibliographical note

Funding Information:
Acknowledgments. This work was supported in part by the National Natural Science Foundation of China under grant Nos. 61671182 and 61471146.

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

Keywords

  • Blind image restoration
  • Face hallucination
  • Flow field

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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