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Lightweight Separable Convolutional Dehazing Network to Mobile FPGA

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Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14498))

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Abstract

The advancement of deep learning has significantly increased the efficiency of picture dehazing techniques. Convolutional neural networks can’t, however, be implemented on portable FPGA devices because to their high computing, storage, and energy needs. In this paper, we propose a generic solution for image dehazing from CNN models to mobile FPGAs. The proposed solution designs lightweight network using depth-wise separable convolution and channel attention mechanism, and uses an accelerator to increase the system’s processing efficiency. We implemented the entire system on a custom and low-cost FPGA SOC platform (Xilinx Inc. ZYNQ\(^{TM}\) XC7Z035). Experiments can conclude that our approach has compatible performance to GPU-based methods with much lower resource usage.

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Acknowledgments

This work was supported by the Natural Science Foundation of China (62202347) and the Natural Science Foundation of Hubei Province (2022CFB578).

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081, China

    Xinrui Ju, Wei Wang & Xin Xu

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan, 430081, China

    Wei Wang & Xin Xu

Authors
  1. Xinrui Ju
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  2. Wei Wang
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  3. Xin Xu
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Corresponding author

Correspondence to Wei Wang .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneve, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

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Ju, X., Wang, W., Xu, X. (2024). Lightweight Separable Convolutional Dehazing Network to Mobile FPGA. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14498. Springer, Cham. https://doi.org/10.1007/978-3-031-50078-7_10

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  • DOI: https://doi.org/10.1007/978-3-031-50078-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50077-0

  • Online ISBN: 978-3-031-50078-7

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