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Underwater Image Enhancement Based on the Fusion of PUIENet and NAFNet

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Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14495))

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Abstract

Due to light absorption and scattering in the ocean, underwater images suffer from blur and color bias, and the colors tend to be biased towards blue or green. To enhance underwater images, many underwater image enhancement (UIE) methods have been developed. Probabilistic Network for UIE (PUIENet) is a neural network model that produces good results in processing underwater images. However, it cannot handle underwater images with motion blur, which is caused by camera or object motion. Nonlinear Activation Free Network (NAFNet) is a network model designed to remove image blur by simplifying everything. Inspired by NAFNet, we simplified the convolution, activation function, and channel attention module of PUIENet, resulting in Probabilistic and Nonlinear Activation Hybrid for UIE (PNAH_UIE), which reduced training time by approximately 19\(\%\) and also reduced loss. In this paper, we propose a deep learning-based method for underwater image enhancement, called Probabilistic and Nonlinear Activation Hybrid Network for UIE (PNAHNet_UIE), which integrates the two most advanced network structures, PNAH_UIE and NAFNet, to improve overall image clarity and remove motion blur. The URPC2022 dataset was used in the experiments, which comes from the “CHINA UNDERWATER ROBOT PROFESSIONAL CONTEST.” PNAH_UIE was used to enhance the URPC2022 dataset, and the processed images were checked for motion blur. If the variance of an image was below a certain threshold, the NAFNet network was used to process the image, thus reducing computational pressure.

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Authors and Affiliations

  1. School of Computer Science, Inner Mongolia University, Hohhot, 010000, China

    Chao Li & Bo Yang

Authors
  1. Chao Li
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  2. Bo Yang
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Correspondence to Bo Yang .

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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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Li, C., Yang, B. (2024). Underwater Image Enhancement Based on the Fusion of PUIENet and NAFNet. 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 14495. Springer, Cham. https://doi.org/10.1007/978-3-031-50069-5_28

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  • DOI: https://doi.org/10.1007/978-3-031-50069-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50068-8

  • Online ISBN: 978-3-031-50069-5

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