Laser & Optoelectronics Progress, Volume. 59, Issue 22, 2215004(2022)

Underwater Image Enhancement Based on Dense Cascaded Convolutional Neural Network

Qingjiang Chen and Yali Xie*
Author Affiliations
  • School of Science, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China
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    Figures & Tables(13)
    Example of autoencoder (AE)
    Feature extraction network
    Dense block structure
    Structure of texture refinement. (a) Texture refinement network; (b) texture refinement unit
    Degraded underwater image, clear underwater image corresponding to degraded underwater image, HSV color space image corresponding to degraded underwater image and its component images. (a) Degraded images, (b) corresponding clear underwater images, (c) corresponding HSV color space images, (d) corresponding H component images, (e) corresponding S component images, and (f) corresponding V component images of coral and whale skeleton
    Flow chart of proposed algorithm
    Experimental results of different algorithms. (a) Original images; (b) CLAHE; (c) UDCP; (d) FE; (e) CycleGAN; (f) WSCT; (g) prposed algorithm
    Diagram of different models. (a) model2; (b) model3; (c) model4; (d) model5
    Different model's results. (a) model2's result; (b) model3's result; (c) model4's result; (d) model5's result; (e) proposed model's result
    Objective evaluation indicators for different number of dense blocks
    • Table 1. UICQE comparison of results of proposed method and several other algorithms

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      Table 1. UICQE comparison of results of proposed method and several other algorithms

      UCIQECLAHEUDCPFECycleGANWSCTProposed
      Image10.4380.4800.5940.6200.6020.597
      Image20.4130.4530.6160.6170.6030.664
      Image30.5210.5530.5860.6100.6120.613
      Image40.4750.6160.6140.6580.5560.603
      Image50.5630.6410.6030.6010.6180.638
      Image60.4910.4170.5970.5930.5190.603
      Image70.5490.6080.6470.5840.5960.624
      Image80.4890.5640.5790.5480.5570.593
      Average0.4923750.5415000.6045000.6038750.5828750.616875
    • Table 2. UIQM comparison of results of proposed method and several other algorithms

      View table

      Table 2. UIQM comparison of results of proposed method and several other algorithms

      UIQMCLAHEUDCPFECycleGANWSCTProposed
      Image11.4231.2484.3154.3063.2374.953
      Image20.4810.8554.1833.7942.9004.385
      Image35.5605.5745.4905.5555.6585.960
      Image44.8094.1395.2504.1664.9024.921
      Image54.9144.5544.4964.2104.7085.297
      Image64.9594.9734.3934.3664.4215.015
      Image74.7834.9824.9624.9254.8585.307
      Image85.1314.9035.3995.2095.1535.738
      Average4.0075003.9035004.8110004.5663754.4796255.197000
    • Table 3. Subjective evaluation index comparison of results of proposed model and several other models

      View table

      Table 3. Subjective evaluation index comparison of results of proposed model and several other models

      Evaluation indexUCIQEUIQMTime /s
      Model10.58615.21450.53
      Model20.49274.87130.49
      Model30.51464.98570.51
      Model40.60275.30640.60
      Model50.50615.01490.52
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    Qingjiang Chen, Yali Xie. Underwater Image Enhancement Based on Dense Cascaded Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2215004

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    Paper Information

    Category: Machine Vision

    Received: Jul. 26, 2021

    Accepted: Oct. 13, 2021

    Published Online: Oct. 12, 2022

    The Author Email: Yali Xie (2696453994@qq.com)

    DOI:10.3788/LOP202259.2215004

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