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UHD Underwater Image Enhancement via Frequency-Spatial Domain Aware Network

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13843))

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Abstract

Currently, carrying ultra high definition (UHD) imaging equ- ipment to record rich environmental conditions in deep water has become a hot issue in underwater exploration. However, due to the poor light transmission in deep water spaces and the large number of impurity particles, UHD underwater imaging is often plagued by low contrast and blur. To overcome these challenges, we propose an efficient two-path model (UHD-SFNet) that recovers the color and the texture of an underwater blurred image in the frequency and the spatial domains. Specifically, the method consists of two branches: in the first branch, we use a bilateral enhancement pipeline that extracts the frequency domain information of a degraded image to reconstruct clear textures. In the pipeline, we embed 1D convolutional layers in the MLP-based framework to capture the local characteristics of the token sequence. In the second branch, we develop U-RSGNet to capture the color features of the image after Gaussian blurring to generate a feature map rich in color information. Finally, the extracted texture features are fused with the color features to produce a clear underwater image. In addition, to construct paired high-quality underwater image enhancement dataset, we propose UHD-CycleGAN with the help of domain adaptation to produce more realistic UHD synthetic images. Experimental results show that our algorithm outperforms existing methods significantly in underwater image enhancement on a single GPU with 24G RAM. Codes are available at https://github.com/wyw0112/UHD-SFNet.

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Acknowledgements

This work was supported by National Key Reserach and Development Program of China (2019YFB1706900), National Natural Science Foundation of China (62176123), Fundamental Research Funds for the Central Universities (30920021131) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX22_0461).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Yiwen Wei, Zhuoran Zheng & Xiuyi Jia

Authors
  1. Yiwen Wei
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  2. Zhuoran Zheng
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  3. Xiuyi Jia
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Corresponding author

Correspondence to Xiuyi Jia .

Editor information

Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Wei, Y., Zheng, Z., Jia, X. (2023). UHD Underwater Image Enhancement via Frequency-Spatial Domain Aware Network. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13843. Springer, Cham. https://doi.org/10.1007/978-3-031-26313-2_2

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  • DOI: https://doi.org/10.1007/978-3-031-26313-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26312-5

  • Online ISBN: 978-3-031-26313-2

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