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Wavelet-Based Mamba with Fourier Adjustment for Low-Light Image Enhancement

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Computer Vision – ACCV 2024 (ACCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15475))

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Abstract

Frequency information (e.g., Discrete Wavelet Transform and Fast Fourier Transform) has been widely applied to solve the issue of Low-Light Image Enhancement (LLIE). However, existing frequency-based models primarily operate in the simple wavelet or Fourier space of images, which lacks utilization of valid global and local information in each space. We found that wavelet frequency information is more sensitive to global brightness due to its low-frequency component while Fourier frequency information is more sensitive to local details due to its phase component. In order to achieve superior preliminary brightness enhancement by optimally integrating spatial channel information with low-frequency components in the wavelet transform, we introduce channel-wise Mamba, which compensates for the long-range dependencies of CNNs and has lower complexity compared to Diffusion and Transformer models. So in this work, we propose a novel Wavelet-based Mamba with Fourier Adjustment model called WalMaFa, consisting of a Wavelet-based Mamba Block (WMB) and a Fast Fourier Adjustment Block (FFAB). We employ an Encoder-Latent-Decoder structure to accomplish the end-to-end transformation. Specifically, WMB is adopted in the Encoder and Decoder to enhance global brightness while FFAB is adopted in the Latent to fine-tune local texture details and alleviate ambiguity. Extensive experiments demonstrate that our proposed WalMaFa achieves state-of-the-art performance with fewer computational resources and faster speed. Code is now available at: https://github.com/mcpaulgeorge/WalMaFa.

J. Tan and S. Pei—Contribute equally.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their invaluable comments. This work was partially funded by the National Natural Science Foundation of China under Grant No. 61975124, State Key Laboratory of Computer Architecture (ICT, CAS) under Grant No. CARCHA202111, Engineering Research Center of Software/Hardware Co-design Technology and Application, Ministry of Education, East China Normal University under Grant No. OP202202, and Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety under Grant No. 2023ZDSYSKFKT04.

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Authors and Affiliations

  1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Junhao Tan, Songwen Pei & Wei Qin

  2. School of Computer Science and Artificial Intelligence, Liaoning Normal University, Liaoning, 116081, China

    Bo Fu

  3. College of Computer Science and Technology, Jilin University, Jilin, 134000, China

    Ximing Li

  4. School of Computer, National University of Dense Technology, Changsha, 410073, China

    Libo Huang

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  1. Junhao Tan
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  4. Bo Fu
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Corresponding author

Correspondence to Songwen Pei .

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Editors and Affiliations

  1. University of Science and Technology (POSTECH), Pohang, Korea (Republic of)

    Minsu Cho

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Ivan Laptev

  3. Google, Mountain View, CA, USA

    Du Tran

  4. National University of Singapore, Singapore, Singapore

    Angela Yao

  5. Peking University, Beijing, China

    Hongbin Zha

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Tan, J., Pei, S., Qin, W., Fu, B., Li, X., Huang, L. (2025). Wavelet-Based Mamba with Fourier Adjustment for Low-Light Image Enhancement. In: Cho, M., Laptev, I., Tran, D., Yao, A., Zha, H. (eds) Computer Vision – ACCV 2024. ACCV 2024. Lecture Notes in Computer Science, vol 15475. Springer, Singapore. https://doi.org/10.1007/978-981-96-0911-6_10

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  • DOI: https://doi.org/10.1007/978-981-96-0911-6_10

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