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Deep Specular Highlight Removal for Single Real-world Image

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Published:04 December 2020Publication History

ABSTRACT

Specular highlight removal is a challenging task. We present a novel data-driven approach for automatic specular highlight removal from a single image. To this end, we build a new dataset of real-world images for specular highlight removal with corresponding ground-truth diffuse images. Based on the dataset, we also present a specular highlight removal network by introducing the detection of specular reflections information as guidance. The experimental evaluations indicate that the proposed approach outperforms recent state-of-the-art methods.

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References

  1. Isabel Funke, Sebastian Bodenstedt, Carina Riediger, Jürgen Weitz, and Stefanie Speidel. 2018. Generative adversarial networks for specular highlight removal in endoscopic images. In Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, Vol. 10576. 1057604.Google ScholarGoogle Scholar
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  3. Othmane Meslouhi, Mustapha Kardouchi, Hakim Allali, Taoufiq Gadi, and Yassir Benkaddour. 2011. Automatic detection and inpainting of specular reflections for colposcopic images. Open Computer Science 1, 3 (2011), 341–354.Google ScholarGoogle ScholarCross RefCross Ref
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  • Published in

    cover image ACM Conferences
    SA '20: SIGGRAPH Asia 2020 Posters
    December 2020
    78 pages
    ISBN:9781450381130
    DOI:10.1145/3415264

    Copyright © 2020 Owner/Author

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 4 December 2020

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    Qualifiers

    • poster
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate178of869submissions,20%

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