Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866914965318795264 |
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| author | Reclik, Tom Medghalchi, Setareh Schumacher, Philipp Wollenweber, Maximilian Al-Samman, Talal Korte-Kerzel, Sandra Kerzel, Ulrich |
| author_facet | Reclik, Tom Medghalchi, Setareh Schumacher, Philipp Wollenweber, Maximilian Al-Samman, Talal Korte-Kerzel, Sandra Kerzel, Ulrich |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03746 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence Reclik, Tom Medghalchi, Setareh Schumacher, Philipp Wollenweber, Maximilian Al-Samman, Talal Korte-Kerzel, Sandra Kerzel, Ulrich Image and Video Processing Materials Science 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. |
| title | Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence |
| topic | Image and Video Processing Materials Science |
| url | https://arxiv.org/abs/2410.03746 |