• Login
    View Item 
    •   Eurographics DL Home
    • Computer Graphics Forum
    • Volume 38 (2019)
    • 38-Issue 7
    • View Item
    •   Eurographics DL Home
    • Computer Graphics Forum
    • Volume 38 (2019)
    • 38-Issue 7
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Naturalness-Preserving Image Tone Enhancement Using Generative Adversarial Networks

    Thumbnail
    View/Open
    v38i7pp277-285.pdf (67.92Mb)
    3_additional_supplementary_material.pdf (2.567Mb)
    html.zip (68.41Mb)
    Date
    2019
    Author
    Son, Hyeongseok
    Lee, Gunhee
    Cho, Sunghyun
    Lee, Seungyong ORCID
    Pay-Per-View via TIB Hannover:

    Try if this item/paper is available.

    Metadata
    Show full item record
    Abstract
    This paper proposes a deep learning-based image tone enhancement approach that can maximally enhance the tone of an image while preserving the naturalness. Our approach does not require carefully generated ground-truth images by human experts for training. Instead, we train a deep neural network to mimic the behavior of a previous classical filtering method that produces drastic but possibly unnatural-looking tone enhancement results. To preserve the naturalness, we adopt the generative adversarial network (GAN) framework as a regularizer for the naturalness. To suppress artifacts caused by the generative nature of the GAN framework, we also propose an imbalanced cycle-consistency loss. Experimental results show that our approach can effectively enhance the tone and contrast of an image while preserving the naturalness compared to previous state-of-the-art approaches.
    BibTeX
    @article {10.1111:cgf.13836,
    journal = {Computer Graphics Forum},
    title = {{Naturalness-Preserving Image Tone Enhancement Using Generative Adversarial Networks}},
    author = {Son, Hyeongseok and Lee, Gunhee and Cho, Sunghyun and Lee, Seungyong},
    year = {2019},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.13836}
    }
    URI
    https://doi.org/10.1111/cgf.13836
    https://diglib.eg.org:443/handle/10.1111/cgf13836
    Collections
    • 38-Issue 7

    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA
     

     

    Browse

    All of Eurographics DLCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    BibTeX | TOC

    Create BibTeX Create Table of Contents

    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA