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Partial Scan Electron Microscopy with Deep Learning
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Ede, Jeffrey M. (2019) Partial Scan Electron Microscopy with Deep Learning. Working Paper. arXiv. (Unpublished)
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Official URL: https://github.com/Jeffrey-Ede/DLSS-STEM
Abstract
Compressed sensing can increase resolution, and decrease electron dose and scan time of electron microscope point-scan systems with minimal information loss. Building on a history of successful deep learning applications in compressed sensing, we have developed a two-stage multiscale generative adversarial network to supersample scanning transmission electron micrographs with point-scan coverage reduced to 1/16, 1/25, ..., 1/100 px. We propose a novel non-adversarial learning policy to train a unified generator for multiple coverages and introduce an auxiliary network to homogenize prioritization of training data with varied signal-to-noise ratios. This achieves root mean square errors of 3.23% and 4.54% at 1/16 px and 1/100 px coverage, respectively; within 1% of errors for networks trained for each coverage individually. Detailed error distributions are presented for unified and individual coverage generators, including errors per output pixel. In addition, we present a baseline one-stage network for a single coverage and investigate numerical precision for web serving. Source code, training data, and pretrained models are publicly available at this https URL [https://github.com/Jeffrey-Ede/DLSS-STEM].
| Item Type: | Working or Discussion Paper (Working Paper) | ||||
|---|---|---|---|---|---|
| Divisions: | Faculty of Science > Physics | ||||
| Journal or Publication Title: | arXiv preprint arXiv:1910.10467 | ||||
| Publisher: | arXiv | ||||
| Official Date: | 2019 | ||||
| Dates: |
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| Number: | 1910.10467 | ||||
| Institution: | University of Warwick | ||||
| Status: | Not Peer Reviewed | ||||
| Publication Status: | Unpublished | ||||
| Access rights to Published version: | Open Access | ||||
| Description: | Preprint on using deep learning to complete scanning transmission electron micrographs from partial scans. This can be used to decrease electron dose and scan time, to reduce damage to samples. It introduces a large new dataset of scanning transmission electron micrographs for machine learnings. |
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