Optics and Precision Engineering, Volume. 30, Issue 22, 2939(2022)

Coarse-to-fine underwater image enhancement based on multi-level wavelet transform

Guoming YUAN1, Guang YANG2、*, Jinfeng WANG2, Haijun LIU1, and Wei WANG2
Author Affiliations
  • 1Department of Emergency Management, Institute of Disaster Prevention, Sanhe06520, China
  • 2Department of Information Engineering, Institute of Disaster Prevention, Sanhe06501, China.
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    Figures & Tables(14)
    Architecture of coarse-to-fine network for underwater image enhancement based on multi-level wavelet transform
    Comparison between initial images and enhanced images
    High frequency image obtained by wavelet transform at different levels
    Architecture of RK2 block
    Loss curves during training.
    Average PSNR of testing data during training.
    Testing samples
    Comparison of enhanced results on synthetic underwater images
    Comparison of enhanced results on real underwater images
    PSNR of different algorithms on Test1 and Test2.
    • Table 1. Comparison of quantitative results by variant models with different number of building modules on Test 1.

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      Table 1. Comparison of quantitative results by variant models with different number of building modules on Test 1.

      LevelResIN_3ResIN_4ResIN_5
      PSNR24.3824.4724.50
      SSIM0.884 10.885 60.886 7
      ModelRes_3Res_4Res_6
      PSNR24.2324.4424.51
      SSIM0.882 50.884 90.886 8
      ModelRK2_3RK2_4RK2_6
      PSNR24.0124.1824.53
      SSIM0.874 30.879 20.886 8
    • Table 2. Comparison of quantitative results by different variant models on Test1.

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      Table 2. Comparison of quantitative results by different variant models on Test1.

      Leveln=1n=2n=3n=4n=5
      PSNR21.0823.3324.5124.4824.43
      SSIM0.830 40.866 70.886 70.881 70.876 4
      Modeln=6Net-w/o-lbNet-w/o-hbNet-w/o-RNet-Residual
      PSNR23.8922.4722.1521.7623.87
      SSIM0.871 10.841 20.839 70.838 90.869 5
    • Table 3. Quantitative comparison of enhanced results by different algorithms on Test1.

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      Table 3. Quantitative comparison of enhanced results by different algorithms on Test1.

      ModelsEWTERHHUIEUWCNNUWGANWDNMWNFGANOurs
      PSNR21.0415.2511.4815.8712.1918.9823.6714.8424.51
      SSIM0.8360.7150.6350.7050.6480.7760.8640.7320.886
      PCQI0.9910.9830.9680.9740.9710.9871.0120.8921.057
      EI78.4574.1866.5271.5768.2976.4480.5764.0882.54
      UIQM2.9512.6162.3952.5532.4902.8213.0522.7953.123
      UCIQE0.6100.5940.5660.5810.5730.6050.6140.6080.625
      Entropy7.5287.2467.0297.1267.0977.4827.7147.3037.854
    • Table 4. Quantitative comparison of enhanced results by different algorithms on Test2.

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      Table 4. Quantitative comparison of enhanced results by different algorithms on Test2.

      ModelsEWTERHHUIEUWCNNUWGANWDNMWNFGANOurs
      PSNR17.6417.8418.5114.2317.3217.9618.2217.8120.18
      SSIM0.8230.8470.8030.6570.8220.7800.8090.7300.862
      PCQI0.9891.0180.9860.9690.9770.9800.9810.9831.035
      EI76.2077.6574.8968.4770.8973.4975.1275.8980.44
      UIQM2.8712.9212.8272.4562.7102.7332.8022.8142.987
      UCIQE0.6070.6130.6050.5870.5980.6010.6090.6030.618
      Entropy7.3897.4577.3387.0207.1177.2487.3087.3257.528
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    Guoming YUAN, Guang YANG, Jinfeng WANG, Haijun LIU, Wei WANG. Coarse-to-fine underwater image enhancement based on multi-level wavelet transform[J]. Optics and Precision Engineering, 2022, 30(22): 2939

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    Paper Information

    Category: Information Sciences

    Received: May. 19, 2022

    Accepted: --

    Published Online: Nov. 28, 2022

    The Author Email: YANG Guang (yangg202205@163.com)

    DOI:10.37188/OPE.20223022.2939

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