Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation
This work addresses the computational bottleneck in STEM simulation for materials science researchers, but it is incremental as it matches rather than surpasses existing regression methods.
The paper tackles the problem of slow and inaccurate STEM image simulation by using a Generative Adversarial Network (GAN) to translate faster but less accurate convolution-based images to high-accuracy multislice images, achieving similar accuracy to previous regression models on the same dataset.
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.