Investigating loss functions for extreme super-resolution

Younghyun Jo, Sejong Yang, Seon Joo Kim

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

41 Citations (Scopus)

Abstract

The performance of image super-resolution (SR) has been greatly improved by using convolutional neural networks. Most of the previous SR methods have been studied up to ×4 upsampling, and few were studied for ×16 upsampling. The general approach for perceptual ×4 SR is using GAN with VGG based perceptual loss, however, we found that it creates inconsistent details for perceptual ×16 SR. To this end, we have investigated loss functions and we propose to use GAN with LPIPS [23] loss for perceptual extreme SR. In addition, we use U-net structure discriminator [14] together to consider both the global and local context of an input image. Experimental results show that our method outperforms the conventional perceptual loss, and we achieved second and first place in the LPIPS and PI measures respectively for NTIRE 2020 perceptual extreme SR challenge.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PublisherIEEE Computer Society
Pages1705-1712
Number of pages8
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

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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