• Acta Optica Sinica
  • Vol. 43, Issue 5, 0518002 (2023)
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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    DOI: 10.3788/AOS221657 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    Model structure of GANs
    Fig. 1. Model structure of GANs
    Model structure of Cycle-GANs
    Fig. 2. Model structure of Cycle-GANs
    Model illustration of Cycle-GANs
    Fig. 3. Model illustration of Cycle-GANs
    Graded scaling of image resolution and prediction effect
    Fig. 4. Graded scaling of image resolution and prediction effect
    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
    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
    Results of 25× HR generated from 10× LR
    Fig. 7. Results of 25× HR generated from 10× LR
    Results of 25× HR generated from 4× LR
    Fig. 8. Results of 25× HR generated from 4× LR
    TypeDescription
    Image size(900,900,3)
    OptimizerAdam
    Learning rate0.0002
    Batch size1
    Epoch1000
    Decay epoch1000
    Crop size240
    Table 1. Training parameters of image network model
    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 2. Objective evaluation indexes of theory verification
    Image groupSSIMPSNR /dBNRMSE
    10.68423.5230.131
    20.65723.0630.137
    30.60322.2790.138
    Table 3. Objective evaluation indexes of 25× HR generated from 10× LR
    Image groupSSIMPSNR /dBNRMSE
    10.59722.3220.180
    20.59622.2790.182
    30.58521.7970.184
    Table 4. Objective evaluation indexes of 25× HR generated from 4× LR
    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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