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Self-Supervised Dehazing Network Using Physical Priors

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13843))

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Abstract

In this paper, we propose a lightweight self-supervised dehazing network with the help of physical priors, called Self-Supervised Dehazing Network (SSDN). SSDN is a modified U-Net that estimates a clear image, transmission map, and atmospheric airlight out of the input hazy image based on the Atmospheric Scattering Model (ASM). It is trained in a self-supervised manner, utilizing recent self-supervised training methods and physical prior knowledge for obtaining realistic outputs. Thanks to the training objectives based on ASM, SSDN learns physically meaningful features. As a result, SSDN learns to estimate clear images that satisfy physical priors, instead of simply following data distribution, and it becomes generalized well over the data domain. With the self-supervision of SSDN, the dehazing performance can be easily finetuned with an additional dataset that can be built by simply collecting hazy images. Experimental results show that our proposed SSDN is lightweight and shows competitive dehazing performance with strong generalization capability over various data domains.

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Acknowledgements

We would like to thank the anonymous reviewers for their constructive comments. This work was supported by IITP grants (SW Star Lab, 2015-0-00174; AI Innovation Hub, 2021-0-02068; AI Graduate School Program (POSTECH), 2019-0-01906), KOCCA grant (R2021040136), NRF grant (NRF-2021R1F1A1048120) from Korea government (MSIT and MCST), and POSCO ICT COMPANY LTD.

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Authors and Affiliations

  1. POSTECH, Pohang, Korea

    Gwangjin Ju, Donggun Lee & Seungyong Lee

  2. POSCO ICT, Pohang, Korea

    Yeongcheol Choi & Jee Hyun Paik

  3. Yeungnam University, Gyeongsan, Korea

    Gyeongha Hwang

Authors
  1. Gwangjin Ju
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  2. Yeongcheol Choi
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  3. Donggun Lee
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  4. Jee Hyun Paik
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  5. Gyeongha Hwang
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  6. Seungyong Lee
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Corresponding author

Correspondence to Seungyong Lee .

Editor information

Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Ju, G., Choi, Y., Lee, D., Paik, J.H., Hwang, G., Lee, S. (2023). Self-Supervised Dehazing Network Using Physical Priors. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13843. Springer, Cham. https://doi.org/10.1007/978-3-031-26313-2_18

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  • DOI: https://doi.org/10.1007/978-3-031-26313-2_18

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  • Online ISBN: 978-3-031-26313-2

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