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Adaptive partial scanning transmission electron microscopy with reinforcement learning
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Ede, Jeffrey M. (2020) Adaptive partial scanning transmission electron microscopy with reinforcement learning. Working Paper. Cornell University: arXiv. (Submitted)
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Official URL: https://arxiv.org/abs/2004.02786
Abstract
Compressed sensing can decrease scanning transmission electron microscopy electron dose and scan time with minimal information loss. Traditionally, sparse scans used in compressed sensing sample a static set of probing locations. In contrast, we present a prototype for a contiguous sparse scan system that piecewise adapts scan paths to specimens as they are scanned. Sampling directions for scan segments are chosen by a recurrent neural network based on previously observed scan segments. The recurrent actor is trained by reinforcement learning to cooperate with a feedforward convolutional neural network that completes sparse scans. This paper presents our learning policy, experiments, and example partial scans, and discusses future research directions. Source code, pretrained models, and training data is openly accessible at https://github.com/Jeffrey-Ede/adaptive-scans.
| Item Type: | Working or Discussion Paper (Working Paper) | |||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QC Physics Q Science > QH Natural history T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > Physics | |||||||||
| Library of Congress Subject Headings (LCSH): | Compressed sensing (Telecommunication), Machine learning, Electron microscopy , Reinforcement learning , Scanning transmission electron microscopy | |||||||||
| Publisher: | arXiv | |||||||||
| Place of Publication: | Cornell University | |||||||||
| Official Date: | 22 December 2020 | |||||||||
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| Number of Pages: | 12 | |||||||||
| Institution: | University of Warwick | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Submitted | |||||||||
| Copyright Holders: | Jeffrey M. Ede | |||||||||
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