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TCL-Net: A Lightweight and Efficient Dehazing Network with Frequency-Domain Fusion and Multi-Angle Attention

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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

Hazy images present a challenging ill-posed problem, suffering from information loss and color distortion. Current deep learning-based dehazing methods enhance performance by increasing network depth but incur substantial parameter overhead. Meanwhile, standard convolutional layers concentrate on low-frequency details, often overlooking high-frequency information, which hinders the effective utilization of prior information presented in blurred images. In this paper, we propose TCL-Net, a lightweight dehazing network which emphasizes on frequency-domain features. Our network first includes a sophisticated layer for extracting high-frequency and low-frequency information, specifically designed using Fast Vision Transformers for the original blurred images. Concurrently, we have designed a frequency-domain information fusion module that integrates high-frequency and low-frequency information with the characteristics of convolutional networks for subsequent convolutional layers. Furthermore, to better leverage spatial information of the original image, we introduce a multi-angle attention module. With the aforementioned design, our network achieves superior performance with a total parameter size of only 0.48 MB, representing an order of magnitude reduction in parameters compared to other state-of-the-art lightweight networks.

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Acknowledgments

This work was supported in part by the National Key R&D Program of China under Grants 2022YFB4501600 and 2022YFB4501603, in part by the National Natural Science Foundation of China under Grants 62102383, 61976200, and 62172380.

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Cheng Tang & Wenqi Lou

  2. Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China

    Cheng Tang & Wenqi Lou

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  1. Cheng Tang
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  2. Wenqi Lou
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Correspondence to Wenqi Lou .

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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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Tang, C., Lou, W. (2025). TCL-Net: A Lightweight and Efficient Dehazing Network with Frequency-Domain Fusion and Multi-Angle Attention. 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_12

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  • DOI: https://doi.org/10.1007/978-981-96-0911-6_12

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