  The use of accurate scanning transmission electron microscopy (STEM) image
simulation methods require large computation times that can make their use
infeasible for the simulation of many images. Other simulation methods based on
linear imaging models, such as the convolution method, are much faster but are
too inaccurate to be used in application. In this paper, we explore deep
learning models that attempt to translate a STEM image produced by the
convolution method to a prediction of the high accuracy multislice image. We
then compare our results to those of regression methods. We find that using the
deep learning model Generative Adversarial Network (GAN) provides us with the
best results and performs at a similar accuracy level to previous regression
models on the same dataset. Codes and data for this project can be found in
this GitHub repository, https://github.com/uw-cmg/GAN-STEM-Conv2MultiSlice.
