Nature Communications (May 2024)

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,
  • Qionghai Dai

DOI
https://doi.org/10.1038/s41467-024-48575-9
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 15

Abstract

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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.