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arXiv · 2410.03746

Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence

Tom Reclik·Setareh Medghalchi·Philipp Schumacher·Maximilian Wollenweber·Talal Al-Samman·Sandra Korte-Kerzel·Ulrich Kerzel
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Abstract

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.

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Tom Reclik, Setareh Medghalchi, Philipp Schumacher, Maximilian Wollenweber, Talal Al-Samman, Sandra Korte-Kerzel, Ulrich Kerzel. 2024-10-01. Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence. https://doi.org/10.1016/j.matdes.2025.113955

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