  Compressed sensing algorithms are used to decrease electron microscope scan
time and electron beam exposure with minimal information loss. Following
successful applications of deep learning to compressed sensing, we have
developed a two-stage multiscale generative adversarial neural network to
complete realistic 512$\times$512 scanning transmission electron micrographs
from spiral, jittered gridlike, and other partial scans. For spiral scans and
mean squared error based pre-training, this enables electron beam coverage to
be decreased by 17.9$\times$ with a 3.8\% test set root mean squared intensity
error, and by 87.0$\times$ with a 6.2\% error. Our generator networks are
trained on partial scans created from a new dataset of 16227 scanning
transmission electron micrographs. High performance is achieved with adaptive
learning rate clipping of loss spikes and an auxiliary trainer network. Our
source code, new dataset, and pre-trained models have been made publicly
available at https://github.com/Jeffrey-Ede/partial-STEM
