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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    References(22)

    [1] D X Lu, W H Fang, Y Y Li et al. Optical coherence tomography: principles and recent developments. Chin Opt, 13, 919-935(2020).

    [2] Y Q Huang, Z X Lu, Z M Shao et al. Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network. Opt Express, 27, 12289-12307(2019).

    [3] V Das, S Dandapat, P K Bora. Unsupervised super-resolution of OCT images using generative adversarial network for improved age-related macular degeneration diagnosis. IEEE Sensors J, 20, 8746-8756(2020).

    [4] B Qiu, Y F You, Z Y Huang et al. N2NSR‐OCT: simultaneous denoising and super‐resolution in optical coherence tomography images using semisupervised deep learning. J Biophotonics, 14, e202000282(2021).

    [5] Y Q Lu, M H Chen, K B Qin et al. Super-resolution reconstruction of OCT image based on pyramid long-range transformer. Chin J Lasers, 50, 1507107(2023).

    [6] S T Ke, M H Chen, Z X Zheng et al. Super-resolution reconstruction of optical coherence tomography retinal images by generating adversarial network. Chin J Lasers, 49, 1507203(2022).

    [7] Y H Ma, X J Chen, W F Zhu et al. Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN. Biomed Opt Express, 9, 5129-5146(2018).

    [8] R G Wang, H Lei, J Yang. Self-similarity enhancement network for image super-resolution. Opto-Electron Eng, 49, 210382(2022).

    [14] T L Zhao, L Hu, Y M Zhang et al. Super-resolution network with information distillation and multi-scale attention for medical CT image. Sensors, 21, 6870(2021).

    [22] H Bogunović, F Venhuizen, S Klimscha et al. RETOUCH: the retinal OCT fluid detection and segmentation benchmark and challenge. IEEE Trans Med Imaging, 38, 1858-1874(2019).

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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: Chen Minghui (陈明惠)

    DOI:10.12086/oee.2024.240114

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