Fluorescence microscopy is a key driver of discoveries in the life-sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how deep learning enables biological observations beyond the physical limitations of microscopes. On seven concrete examples we illustrate how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how isotropic resolution can be achieved even with a 10-fold under-sampling along the axial direction, and how diffraction-limited structures can be resolved at 20-times higher frame-rates compared to state-of-the-art methods. All described restoration networks are freely available as open source software in Fiji and KNIME.
& Eugene W. Myers1,2,3
1 Center for Systems Biology Dresden (CSBD), Dresden, Germany
2 Max-Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
3 Department of Computer Science, Technical University Dresden
4 MRC Laboratory for Molecular Cell Biology, University College London, London, UK
5 The Francis Crick Institute, London, UK
6 University of Konstanz, Konstanz, Germany
7 CZ Biohub, San Francisco, USA
8 Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
9 Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany
10 German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
* Co-corresponding authors.