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Acta Optica Sinica, Volume. 43, Issue 5, 0518002(2023)

Large-Field Microscopic Imaging Method Based on Cycle Generative Adversarial Networks

Rao Fu1,2, Yu Fang1,2, Yong Yang4, Dong Xiang1,2, and Xiaojing Wu3、*
Author Affiliations
  • 1Institute of Modern Optics, Nankai University, Tianjin 300350, China
  • 2Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Tianjin 300350, China
  • 3Tianjin Union Medical Center, Tianjin 300121, China
  • 4Institute of Intelligent Sensing, Zhejiang Lab, Hangzhou 310013, Zhejiang, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (0)
    References (26)
    Cited By (1)
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    Figures & Tables(12)
    Model structure of GANs

    Fig. 1. Model structure of GANs

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    Model structure of Cycle-GANs

    Fig. 2. Model structure of Cycle-GANs

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    Model illustration of Cycle-GANs

    Fig. 3. Model illustration of Cycle-GANs

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    Graded scaling of image resolution and prediction effect

    Fig. 4. Graded scaling of image resolution and prediction effect

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    Fitting curve of relationship between objective evaluation index and image resolution reduction factor. (a) SSIM; (b) PSNR; (c) NMRSE

    Fig. 5. Fitting curve of relationship between objective evaluation index and image resolution reduction factor. (a) SSIM; (b) PSNR; (c) NMRSE

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    Results of 25× high-resolution virtual images generated from low-resolution images of 10× resolution testing board

    Fig. 6. Results of 25× high-resolution virtual images generated from low-resolution images of 10× resolution testing board

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    Results of 25× HR generated from 10× LR

    Fig. 7. Results of 25× HR generated from 10× LR

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    Results of 25× HR generated from 4× LR

    Fig. 8. Results of 25× HR generated from 4× LR

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    • Table 1. Training parameters of image network model

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      Table 1. Training parameters of image network model

      TypeDescription
      Image size（900，900，3）
      OptimizerAdam
      Learning rate0.0002
      Batch size1
      Epoch1000
      Decay epoch1000
      Crop size240

    • Table 2. Objective evaluation indexes of theory verification

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      Table 2. Objective evaluation indexes of theory verification

      Image groupSSIMPSNR /dBNRMSE
      10.88926.8350.101
      20.83526.5120.109
      30.65524.6260.142
      40.63823.9710.152
      50.40719.5500.241
      60.39818.6310.265

    • Table 3. Objective evaluation indexes of 25× HR generated from 10× LR

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      Table 3. Objective evaluation indexes of 25× HR generated from 10× LR

      Image groupSSIMPSNR /dBNRMSE
      10.68423.5230.131
      20.65723.0630.137
      30.60322.2790.138

    • Table 4. Objective evaluation indexes of 25× HR generated from 4× LR

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      Table 4. Objective evaluation indexes of 25× HR generated from 4× LR

      Image groupSSIMPSNR /dBNRMSE
      10.59722.3220.180
      20.59622.2790.182
      30.58521.7970.184
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    Rao Fu, Yu Fang, Yong Yang, Dong Xiang, Xiaojing Wu. Large-Field Microscopic Imaging Method Based on Cycle Generative Adversarial Networks[J]. Acta Optica Sinica, 2023, 43(5): 0518002

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    Paper Information

    Category: Microscopy

    Received: Aug. 29, 2022

    Accepted: Oct. 14, 2022

    Published Online: Feb. 27, 2023

    The Author Email: Wu Xiaojing (xiaojingwu@nankai.edu.cn)

    DOI:10.3788/AOS221657

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology