Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy

Chang Qiao, Yunmin Zeng, Quan Meng, Xingye Chen, Haoyu Chen, Tao Jiang, Rongfei Wei, Jiabao Guo, Wenfeng Fu, Huaide Lu, Di Li, Yuwang Wang, Hui Qiao, Jiamin Wu, Dong Li () and Qionghai Dai ()
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Chang Qiao: Tsinghua University
Yunmin Zeng: Tsinghua University
Quan Meng: Chinese Academy of Sciences
Xingye Chen: Tsinghua University
Haoyu Chen: Chinese Academy of Sciences
Tao Jiang: Chinese Academy of Sciences
Rongfei Wei: Chinese Academy of Sciences
Jiabao Guo: Chinese Academy of Sciences
Wenfeng Fu: Chinese Academy of Sciences
Huaide Lu: Chinese Academy of Sciences
Di Li: Chinese Academy of Sciences
Yuwang Wang: Tsinghua University
Hui Qiao: Tsinghua University
Jiamin Wu: Tsinghua University
Dong Li: Chinese Academy of Sciences
Qionghai Dai: Tsinghua University

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.

Date: 2024
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DOI: 10.1038/s41467-024-48575-9

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