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Progressive GAN-Based Transfer Network for Low-Light Image Enhancement

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MultiMedia Modeling (MMM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13142))

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Abstract

Images captured in low-light conditions usually suffer from very low contrast and underexpose, which cannot be directly utilized in the subsequent computer vision tasks, such as object recognition, detection, identification and tracking. Existing methods include HE based method, Retinex theory based method and deep learning method which may generate undesirable enhanced results including amplified noise, biased colors and extreme boundary. To address this problem, we utilize prior knowledge of Retinex theory and GAN based on data statistic to propose a progressive GAN-based Transfer network to realize the low-light enhancement. In this paper, the image is decomposed by JieP method based on the Retinex model to obtain the reflection and light components, and learn the relationship between the reflection component of the low-light image and normal light image via a reflection decomposition on network (RefDecN), and then generate the reflection component of the low-light image. Then, another illumination transfering network (IllumTransN) is utilized to transfer the light of normal light image to the reflection component to realize low-light enhancement. Experimental results of low-light image enhancement on RAISE, LOL and MEF datasets demonstrate our ProGAN can outperform state-of-the-art methods in terms of objective and subjective quality.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant 61906009, the Scientific Research Common Program of Beijing Municipal Commission of Education KM202010005018, and the International Research Cooperation Seed Fund of Beijing University of Technology (Project No. 2021B06).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Shuang Jin, Na Qi, Qing Zhu & Haoran Ouyang

  2. Beijing Institute of Artificial Intelligence, Beijing, China

    Na Qi & Qing Zhu

Authors
  1. Shuang Jin
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  2. Na Qi
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  3. Qing Zhu
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  4. Haoran Ouyang
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Corresponding author

Correspondence to Na Qi .

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Editors and Affiliations

  1. IT University of Copenhagen, Copenhagen, Denmark

    Björn Þór Jónsson

  2. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  3. University of Science, VNU-HCM, Ho Chi Minh City, Vietnam

    Minh-Triet Tran

  4. University of Bergen, Bergen, Norway

    Duc-Tien Dang-Nguyen

  5. National Tsing Hua University, Hsinchu, Taiwan

    Anita Min-Chun Hu

  6. Hanoi University of Science and Technology, Hanoi, Vietnam

    Binh Huynh Thi Thanh

  7. Median Technologies, Valbonne, France

    Benoit Huet

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Jin, S., Qi, N., Zhu, Q., Ouyang, H. (2022). Progressive GAN-Based Transfer Network for Low-Light Image Enhancement. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_25

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  • DOI: https://doi.org/10.1007/978-3-030-98355-0_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98354-3

  • Online ISBN: 978-3-030-98355-0

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