  Electron tomography, as an important 3D imaging method, offers a powerful
method to probe the 3D structure of materials from the nano- to the
atomic-scale. However, as a grant challenge, radiation intolerance of the
nanoscale samples and the missing-wedge-induced information loss and artifacts
greatly hindered us from obtaining 3D atomic structures with high fidelity.
Here, for the first time, by combining generative adversarial models with
state-of-the-art network architectures, we demonstrate the resolution of
electron tomography can be improved to 0.71 angstrom which is the highest
three-dimensional imaging resolution that has been reported thus far. We also
show it is possible to recover the lost information and remove artifacts in the
reconstructed tomograms by only acquiring data from -50 to +50 degrees (44%
reduction of dosage compared to -90 to +90 degrees full tilt series). In
contrast to conventional methods, the deep learning model shows outstanding
performance for both macroscopic objects and atomic features solving the
long-standing dosage and missing-wedge problems in electron tomography. Our
work provides important guidance for the application of machine learning
methods to tomographic imaging and sheds light on its applications in other 3D
imaging techniques.
