Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement

Han Ul Kim, Young Jun Koh, Chang Su Kim

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

    53 Citations (Scopus)

    Abstract

    A novel approach for paired and unpaired image enhancement is proposed in this work. First, we develop global enhancement network (GEN) and local enhancement network (LEN), which can faithfully enhance images. The proposed GEN performs the channel-wise intensity transforms that can be trained easier than the pixel-wise prediction. The proposed LEN refines GEN results based on spatial filtering. Second, we propose different training schemes for paired learning and unpaired learning to train GEN and LEN. Especially, we propose a two-stage training scheme based on generative adversarial networks for unpaired learning. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-arts in paired and unpaired image enhancement. Notably, the proposed unpaired image enhancement algorithm provides better results than recent state-of-the-art paired image enhancement algorithms. The source codes and trained models are available at https://github.com/hukim1124/GleNet.

    Original languageEnglish
    Title of host publicationComputer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
    EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages339-354
    Number of pages16
    ISBN (Print)9783030585945
    DOIs
    Publication statusPublished - 2020
    Event16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
    Duration: 2020 Aug 232020 Aug 28

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12370 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference16th European Conference on Computer Vision, ECCV 2020
    Country/TerritoryUnited Kingdom
    CityGlasgow
    Period20/8/2320/8/28

    Bibliographical note

    Publisher Copyright:
    © 2020, Springer Nature Switzerland AG.

    Keywords

    • Generative adversarial network
    • Image enhancement
    • Unpaired learning

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

    Fingerprint

    Dive into the research topics of 'Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement'. Together they form a unique fingerprint.

    Cite this