Opto-Electronic Engineering, Volume. 51, Issue 7, 240114(2024)

Super-resolution reconstruction of retinal OCT image using multi-teacher knowledge distillation network

Minghui Chen1,*... Yanqi Lu1, Wenyi Yang1, Yuanzhu Wang2 and Yi Shao3 |Show fewer author(s)
Author Affiliations
  • 1Shanghai Engineering Research Center of Interventional Medical, Shanghai Institute for Interventional Medical Devices, School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • 2Shanghai Raykeen Laser Technology Co., Ltd., Shanghai 200120, China
  • 3Shanghai General Hospital, Shanghai 200080, China
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    Figures & Tables(9)
    Overall framework of MK-OCT
    Structure of PASRN
    Structure of PANet
    ECD module
    Contrastive learning
    Results of super-resolution reconstruction
    • Table 1. Average performance of various super-resolution models after x4 reconstruction

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      Table 1. Average performance of various super-resolution models after x4 reconstruction

      MethodSize /MBFLOPs /GDataset 1Dataset 2
      PSNRSSIMLPIPSPIPSNRSSIMLPIPSPI
      Bicubic--28.120.78110.4126.79528.430.77300.4226.579
      SRCNN0.20.2328.590.80030.4046.35528.790.79860.3986.297
      CSD12.16122.130.950.81420.3105.67730.900.81190.3275.802
      IMDN2.6541.931.240.82170.2265.55331.210.82200.2305.608
      RFDN1.5932.031.670.82620.2205.21731.780.82170.2175.139
      MK-OCT (Ours)1.4129.832.930.84600.1494.52132.900.84430.1434.443
    • Table 2. Quantitative evaluation of student networks under different conditions after x4 reconstruction

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      Table 2. Quantitative evaluation of student networks under different conditions after x4 reconstruction

      DatasetMetricHR-SMKSingle-teacherNone-CL
      TPSNRTPI
      Dataset 1PSNR31.2732.8832.7732.88
      SSIM0.82380.84590.83960.8457
      LPIPS0.2300.2170.1420.150
      PI5.5935.4404.4574.608
      Dataset 2PSNR31.3332.8732.8132.86
      SSIM0.81780.84240.84110.8420
      LPIPS0.2140.2090.1400.148
      PI5.1465.1294.5614.670
    • Table 3. Average PSRN and PI values of various super-resolution models after reconstruction

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      Table 3. Average PSRN and PI values of various super-resolution models after reconstruction

      MethodPSNRPI
      ×2×4×2×4
      SRCNN33.6728.794.6676.033
      CSD34.2229.984.1095.820
      IMDN35.9031.064.2335.709
      RFDN35.8931.774.0595.455
      MK-OCT (Ours)36.2032.583.9795.103
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    Minghui Chen, Yanqi Lu, Wenyi Yang, Yuanzhu Wang, Yi Shao. Super-resolution reconstruction of retinal OCT image using multi-teacher knowledge distillation network[J]. Opto-Electronic Engineering, 2024, 51(7): 240114

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    Paper Information

    Category: Article

    Received: May. 15, 2024

    Accepted: Aug. 9, 2024

    Published Online: Nov. 12, 2024

    The Author Email: Minghui Chen (陈明惠)

    DOI:10.12086/oee.2024.240114

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