Unsupervised real-world super resolution with cycle generative adversarial network and domain discriminator

Gwantae Kim, Jaihyun Park, Kanghyu Lee, Junyeop Lee, Jeongki Min, Bokyeung Lee, David K. Han, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper proposes an unsupervised single-image Super-Resolution(SR) model using cycleGAN and domain discriminator to solve the problem of SR with unknown degradation using unpaired dataset. In previous approaches, paired dataset is required for training with assumed levels of image degradation. In real world SR applications, however, training sets are typically not of low and high resolution image pairs, but only low resolution images with unknown degradation are provided as inputs. To address the problem, we introduce a cycle-in-cycle GAN based unsupervised learning model using an unpaired dataset. In addition, we combine several losses attributed to image contents, such as pixel-wise loss, VGG feature loss and SSIM loss, for stable learning and performance improvement. We also propose a domain discriminator, which consists of noise discriminator, texture discriminator and color discriminator, to guide generated images to follow target domain distribution rather than source domain. We validate effectiveness of our model in quantitative and qualitative experiments using NTIRE2020 real-world SR challenge dataset.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PublisherIEEE Computer Society
Pages1862-1871
Number of pages10
ISBN (Electronic)9781728193601
DOIs
Publication statusPublished - 2020 Jun
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 - Virtual, Online, United States
Duration: 2020 Jun 142020 Jun 19

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2020-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
Country/TerritoryUnited States
CityVirtual, Online
Period20/6/1420/6/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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