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

Luhong Jin, Bei Liu (), Fenqiang Zhao, Stephen Hahn, Bowei Dong, Ruiyan Song, Timothy C. Elston, Yingke Xu () and Klaus M. Hahn ()
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Luhong Jin: University of North Carolina at Chapel Hill
Bei Liu: University of North Carolina at Chapel Hill
Fenqiang Zhao: University of North Carolina at Chapel Hill
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
Yingke Xu: Zhejiang University
Klaus M. Hahn: University of North Carolina at Chapel Hill

Nature Communications, 2020, vol. 11, issue 1, 1-7

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-15784-x

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