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Brightening the Low-Light Images via a Dual Guided Network

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Artificial Intelligence (CICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13069))

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Abstract

Illumination estimation based on the retinex theory is quite challenging in low-light image enhancement, and thus reflectance adjustment is necessary after illumination removal. In this paper, we propose a dual guided network to address low-light image enhancement. To be concrete, in the first stage of the method, a depth guide is introduced to constrain illumination. Based on their similarity of the smoothness, the accuracy of illumination estimation is improved. For the second stage, an attention guide is injected towards reflectance adjustment to obtain the final enhanced result. Through the guide of the attention module, details and color information lost when removing illumination can be well supplemented. Extensive ablation studies show the effectiveness and rationality of the proposed depth guide and attention guide. Qualitative and quantitative experiments demonstrate our superiority against existing state-of-the-art methods.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Nos. 61922019, 61733002 and 61672125), the LiaoNing Revitalization Talents Program (XLYC1807088) and the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

  1. DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, China

    Risheng Liu & Fan Xin

  2. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China

    Jianing Sun, Jiaao Zhang, Risheng Liu & Fan Xin

  3. School of Software Technology, Dalian University of Technology, Dalian, China

    Jianing Sun & Jiaao Zhang

Authors
  1. Jianing Sun
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  2. Jiaao Zhang
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  3. Risheng Liu
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  4. Fan Xin
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Corresponding author

Correspondence to Risheng Liu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Duke University, Durham, NC, USA

    Yiran Chen

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. University of British Columbia, Vancouver, BC, Canada

    Jane Wang

  5. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

  6. Xidian University, Xi’an, China

    Weisheng Dong

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Sun, J., Zhang, J., Liu, R., Xin, F. (2021). Brightening the Low-Light Images via a Dual Guided Network. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_21

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  • DOI: https://doi.org/10.1007/978-3-030-93046-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93045-5

  • Online ISBN: 978-3-030-93046-2

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