Laser Journal, Volume. 46, Issue 1, 165(2025)

Fluorescence microscopy images super-resolution reconstruction based on unsupervised deep learning

CHEN Dongyu and CHENG Yu*
Author Affiliations
  • Guangdong University of Technology, Guangzhou 510006, China
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    References(13)

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    [7] [7] Wang H, Rivenson Y, Jin Y, et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy[J]. Nature methods, 2019, 16(1): 103-110.

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    [12] [12] Chenyu Y, Guang L, Yi Z, et al. CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN - CIRCLE).[J]. IEEE transactions on medical imaging, 2020, 39(1): 188-203.

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    [14] [14] Zhao W, Zhao S, Li L, et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy[J]. Nature biotechnology, 2022, 40(4): 606-617.

    [15] [15] Ledig C, Theis L, Huszar F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, 2017: 4681-4690.

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    [17] [17] Belharbi S, Whitford M K M, Hoang P, et al. SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution[J]. arXiv preprint arXiv:2406.09168, 2024.

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    CHEN Dongyu, CHENG Yu. Fluorescence microscopy images super-resolution reconstruction based on unsupervised deep learning[J]. Laser Journal, 2025, 46(1): 165

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    Paper Information

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    Received: Aug. 19, 2024

    Accepted: Apr. 17, 2025

    Published Online: Apr. 17, 2025

    The Author Email: CHENG Yu (chengyu@gdut.edu.cn)

    DOI:10.14016/j.cnki.jgzz.2025.01.165

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