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Deep learning supersampled scanning transmission electron microscopy [pre-print]
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Ede, Jeffrey M. (2019) Deep learning supersampled scanning transmission electron microscopy [pre-print]. Working Paper. arXiv. (Unpublished)
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Official URL: https://arxiv.org/abs/1910.10467
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) | ||||
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| Divisions: | Faculty of Science > Physics | ||||
| Publisher: | arXiv | ||||
| Official Date: | 2019 | ||||
| Dates: |
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| Institution: | University of Warwick | ||||
| Status: | Not Peer Reviewed | ||||
| Publication Status: | Unpublished | ||||
| Access rights to Published version: | Open Access | ||||
| Description: | Preprint on supersampling scanning transmission electron micrographs with deep learning. This can be used to decrease electron dose and scan time, to reduce damage to samples. |
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