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Deep learning enables structured illumination microscopy with low light levels and enhanced speed

Author

Listed:
  • Luhong Jin

    (University of North Carolina at Chapel Hill
    Zhejiang University)

  • Bei Liu

    (University of North Carolina at Chapel Hill)

  • Fenqiang Zhao

    (University of North Carolina at Chapel Hill
    Zhejiang University)

  • Stephen Hahn

    (University of North Carolina at Chapel Hill)

  • Bowei Dong

    (University of North Carolina at Chapel Hill)

  • Ruiyan Song

    (University of North Carolina at Chapel Hill)

  • Timothy C. Elston

    (University of North Carolina at Chapel Hill
    Computational Medicine Program, University of North Carolina at Chapel Hill)

  • Yingke Xu

    (Zhejiang University
    Zhejiang University School of Medicine)

  • Klaus M. Hahn

    (University of North Carolina at Chapel Hill)

Abstract

Structured illumination microscopy (SIM) surpasses the optical diffraction limit and offers a two-fold enhancement in resolution over diffraction limited microscopy. However, it requires both intense illumination and multiple acquisitions to produce a single high-resolution image. Using deep learning to augment SIM, we obtain a five-fold reduction in the number of raw images required for super-resolution SIM, and generate images under extreme low light conditions (at least 100× fewer photons). We validate the performance of deep neural networks on different cellular structures and achieve multi-color, live-cell super-resolution imaging with greatly reduced photobleaching.

Suggested Citation

  • Luhong Jin & Bei Liu & Fenqiang Zhao & Stephen Hahn & Bowei Dong & Ruiyan Song & Timothy C. Elston & Yingke Xu & Klaus M. Hahn, 2020. "Deep learning enables structured illumination microscopy with low light levels and enhanced speed," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15784-x
    DOI: 10.1038/s41467-020-15784-x
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    Cited by:

    1. Hao He & Maofeng Cao & Yun Gao & Peng Zheng & Sen Yan & Jin-Hui Zhong & Lei Wang & Dayong Jin & Bin Ren, 2024. "Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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