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

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.

SUBMITTER: Jin L

PROVIDER: S-EPMC7176720 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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

Jin Luhong L Liu Bei B Zhao Fenqiang F Hahn Stephen S Dong Bowei B Song Ruiyan R Elston Timothy C TC Xu Yingke Y Hahn Klaus M KM

Nature communications 20200422 1

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). W ...[more]

PMID: 32321916

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