Scanning Electron Microscopy (SEM) is pivotal in revealing intricate micro- and nanoscale features across various research fields. However, obtaining high-resolution SEM images presents challenges, including prolonged scanning durations and potential sample degradation due to extended electron beam exposure. This paper addresses these challenges by training and applying a deep learning based super-resolution algorithm. We show that the chosen algorithm is capable of increasing the resolution by a factor of 4, thereby reducing the initial imaging time by a factor of 16. We benchmark our method in terms of visual similarity and similarity metrics on two different materials, a dual-phase steel and a case-hardening steel, improving over standard interpolation methods. Additionally, we introduce an experimental pipeline for the study of rare events in scanning electron micrographs, without losing high-resolution information.
@article{arxiv.2410.03746,
title = {Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence},
author = {Tom Reclik and Setareh Medghalchi and Philipp Schumacher and Maximilian Wollenweber and Talal Al-Samman and Sandra Korte-Kerzel and Ulrich Kerzel},
journal= {arXiv preprint arXiv:2410.03746},
year = {2024}
}