GAN with Pixel and Perceptual Regularizations for Photo-Realistic Joint Deblurring and Super-Resolution

Yong Li, Zhenguo Yang, Xudong Mao, Yong Wang, Qing Li, Wenyin Liu, Ying Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In this paper, we propose a Generative Adversarial Network with Pixel and Perceptual regularizations, denoted as P2GAN, to restore single motion blurry and low-resolution images jointly into clear and high-resolution images. It is an end-to-end neural network consisting of deblurring module and super-resolution module, which repairs degraded pixels in the motion-blur images firstly, and then outputs the deblurred images and deblurred features for further reconstruction. More specifically, the proposed P2GAN integrates pixel-wise loss in pixel-level, contextual loss and adversarial loss in perceptual level simultaneously, in order to guide on deblurring and super-resolution reconstruction of the raw images that are blurry and in low-resolution, which help obtaining realistic images. Extensive experiments conducted on a real-world dataset manifest the effectiveness of the proposed approaches, outperforming the state-of-the-art models.

Original languageEnglish
Title of host publicationAdvances in Computer Graphics - 36th Computer Graphics International Conference, CGI 2019, Proceedings
EditorsMarina Gavrilova, Jian Chang, Nadia Magnenat Thalmann, Eckhard Hitzer, Hiroshi Ishikawa
PublisherSpringer-Verlag
Pages395-401
Number of pages7
ISBN (Print)9783030225131
DOIs
Publication statusPublished - 1 Jan 2019
Event36th Computer Graphics International Conference, CGI 2019 - Calgary, Canada
Duration: 17 Jun 201920 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11542 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference36th Computer Graphics International Conference, CGI 2019
Country/TerritoryCanada
CityCalgary
Period17/06/1920/06/19

Keywords

  • Contextual loss
  • GANs
  • Image deblurring
  • Pixel loss
  • Super-resolution

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this