Optics and Precision Engineering, Volume. 30, Issue 12, 1499(2022)

Improved CycleGAN network for underwater microscopic image color correction

Haotian WANG, Qingsheng LIU, Liang CHEN, Wangquan YE*, Yuan LU, Jinjia GUO, and Ronger ZHENG
Author Affiliations
  • College of Physics and Optoelectronic Engineering, Faculty of Information Science and Engineering, Ocean University of China, Qingdao266100, China
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    Figures & Tables(10)
    Structure of cycle-consistent adversarial network
    Structure of improved cycle-consistent adversarial network
    Structure of generator network
    Structure of discriminator network
    Device diagram of underwater microscopic imaging system
    Absorption spectra of typical seawater and blue-green dye inks
    Some self-made target images
    Results of underwater self-made target images processed by different algorithms
    Comparison of underwater natural stone images processed by different algorithms
    • Table 1. Objective evaluation indexes of underwater microscopic images processed by different algorithms

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      Table 1. Objective evaluation indexes of underwater microscopic images processed by different algorithms

      TargetImagePSNRSSIMUCIQE difference *
      Self-made targetImage in air--0
      MSRCR15.582 30.568 20.137 5
      CycleGAN19.579 10.728 50.074 2
      Improved CycleGAN22.103 40.770 60.021 7
      Underwater stoneImage in air--0
      MSRCR15.112 00.399 40.102 6
      CycleGAN16.976 00.664 00.058 4
      Improved CycleGAN18.232 20.702 40.018 3
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    Haotian WANG, Qingsheng LIU, Liang CHEN, Wangquan YE, Yuan LU, Jinjia GUO, Ronger ZHENG. Improved CycleGAN network for underwater microscopic image color correction[J]. Optics and Precision Engineering, 2022, 30(12): 1499

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    Paper Information

    Category: Information Sciences

    Received: Mar. 1, 2022

    Accepted: --

    Published Online: Jul. 5, 2022

    The Author Email: YE Wangquan (yewangquan@ouc.edu.cn)

    DOI:10.37188/OPE.20223012.1499

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