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Over-Exposure Correction via Exposure and Scene Information Disentanglement

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Book cover Computer Vision – ACCV 2020 (ACCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12625))

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Abstract

Over-exposure correction is an important problem of great consequence to social media industries. In this paper, we propose a novel model to tackle this task. Considering that reasonable enhanced results can still vary in terms of exposure, we do not strictly enforce the model to generate identical results with ground-truth images. On the contrary, we train the network to recover the lost scene information according to the existing information of the over-exposure images and generate naturalness-preserved images. Experiments compared with several state-of-the-art methods show the superior performance of the proposed network. Besides, we also verify our hypothesis with ablation studies. Our source code is available at https://github.com/0x437968/overexposure-correction-dise.

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Notes

  1. 1.

    HDR images are required in DRHT [2] and Hdrcnn [3], therefore we can not retrain these two methods.

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Acknowledgements

This work was supported by Key-Area Research and Development Program of Guangdong Province (No.2019B121204008), National Natural Science Foundation of China and Guangdong Province Scientific Research on Big Data (No. U1611461). In addition, we thank the anonymous reviewers for their time and valuable comments.

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Authors and Affiliations

  1. School of Electronics and Computer Engineering, Peking University, Shenzhen, China

    Yuhui Cao, Yurui Ren, Thomas H. Li & Ge Li

  2. Peng Cheng Laboratory, Shenzhen, China

    Yuhui Cao, Yurui Ren & Ge Li

  3. Advanced Institute of Information Technology, Peking University, Hangzhou, China

    Thomas H. Li

Authors
  1. Yuhui Cao
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  2. Yurui Ren
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  3. Thomas H. Li
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  4. Ge Li
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Corresponding author

Correspondence to Ge Li .

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Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Cao, Y., Ren, Y., Li, T.H., Li, G. (2021). Over-Exposure Correction via Exposure and Scene Information Disentanglement. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12625. Springer, Cham. https://doi.org/10.1007/978-3-030-69538-5_25

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  • DOI: https://doi.org/10.1007/978-3-030-69538-5_25

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