  Advances in deep learning have led to remarkable success in augmented
microscopy, enabling us to obtain high-quality microscope images without using
expensive microscopy hardware and sample preparation techniques. However,
current deep learning models for augmented microscopy are mostly U-Net based
neural networks, thus sharing certain drawbacks that limit the performance. In
this work, we introduce global voxel transformer networks (GVTNets), an
advanced deep learning tool for augmented microscopy that overcomes intrinsic
limitations of the current U-Net based models and achieves improved
performance. GVTNets are built on global voxel transformer operators (GVTOs),
which are able to aggregate global information, as opposed to local operators
like convolutions. We apply the proposed methods on existing datasets for three
different augmented microscopy tasks under various settings. The performance is
significantly and consistently better than previous U-Net based approaches.
