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Partial scanning transmission electron microscopy with deep learning
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Ede, Jeffrey M. and Beanland, Richard (2020) Partial scanning transmission electron microscopy with deep learning. Scientific Reports, 10 . 8332. doi:10.1038/s41598-020-65261-0 ISSN 2045-2322.
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WRAP-partial-scanning-transmission-electron-microscopy-deep-learning-Ede-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3284Kb) | Preview |
Official URL: http://dx.doi.org/10.1038/s41598-020-65261-0
Abstract
Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a two-stage multiscale generative adversarial neural network to complete realistic 512 × 512 scanning transmission electron micrographs from spiral, jittered gridlike, and other partial scans. For spiral scans and mean squared error based pre-training, this enables electron beam coverage to be decreased by 17.9× with a 3.8% test set root mean squared intensity error, and by 87.0× with a 6.2% error. Our generator networks are trained on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of loss spikes and an auxiliary trainer network. Our source code, new dataset, and pre-trained models are publicly available.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QC Physics Q Science > QH Natural history |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | |||||||||
| Library of Congress Subject Headings (LCSH): | Scanning transmission electron microscopy , Machine learning | |||||||||
| Journal or Publication Title: | Scientific Reports | |||||||||
| Publisher: | Nature Publishing Group | |||||||||
| ISSN: | 2045-2322 | |||||||||
| Official Date: | 20 May 2020 | |||||||||
| Dates: |
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| Volume: | 10 | |||||||||
| Article Number: | 8332 | |||||||||
| DOI: | 10.1038/s41598-020-65261-0 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 1 July 2020 | |||||||||
| Date of first compliant Open Access: | 2 July 2020 | |||||||||
| RIOXX Funder/Project Grant: |
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