Chinese Journal of Liquid Crystals and Displays, Volume. 37, Issue 6, 768(2022)

Underwater image enhancement method based on improved conditions generate adversarial networks

Yan-fei PENG, Jian LI*, Li-rui GU, and Man-ting ZHANG
Author Affiliations
  • School of Electronic and Information Engineering,Liaoning Technical University,Huludao 125105,China
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    Figures & Tables(9)
    CGAN model
    Algorithm flow chart of this paper
    Image pre-processing. (a) Original underwater image; (b) Underwater image after dynamic threshold pre-processing.
    CGAN model of this paper
    Generator structure
    Discriminator structure
    Image comparison before(a)and after(b)processing
    Subjective comparison of the different algorithms
    • Table 1. Comparison of different algorithms’ objective evaluation

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      Table 1. Comparison of different algorithms’ objective evaluation

      MethodMSE(×103PSNR/dBSSIM
      Fusion-based1.128 017.607 70.772 1
      Retinex-based1.292 417.016 80.607 1
      Histogram Prior1.701 915.821 50.539 6
      Blurriness-based1.911 115.318 00.602 9
      GDCP4.016 012.092 90.512 1
      消融实验0.539 420.804 50.754 9
      Ours0.526 022.008 50.797 1
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    Yan-fei PENG, Jian LI, Li-rui GU, Man-ting ZHANG. Underwater image enhancement method based on improved conditions generate adversarial networks[J]. Chinese Journal of Liquid Crystals and Displays, 2022, 37(6): 768

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    Paper Information

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    Received: Dec. 17, 2021

    Accepted: --

    Published Online: Jun. 22, 2022

    The Author Email: Jian LI (1044946077@qq. com)

    DOI:10.37188/CJLCD.2021-0327

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