Format:
1 Online-Ressource (10 Seiten)
Content:
We present a method that lowers the dose required for an electron ptychographic reconstruction by adaptively scanning the specimen, thereby providing the required spatial information redundancy in the regions of highest importance. The proposed method is built upon a deep learning model that is trained by reinforcement learning, using prior knowledge of the specimen structure from training data sets. We show that using adaptive scanning for electron ptychography outperforms alternative low-dose ptychography experiments in terms of reconstruction resolution and quality.
Content:
Peer Reviewed
Note:
The article processing charge was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491192747 and the Open Access Publication Fund of Humboldt-Universität zu Berlin.
In:
[London] : Macmillan Publishers Limited, part of Springer Nature, 2023, 13
Language:
English
DOI:
10.1038/s41598-023-35740-1
URN:
urn:nbn:de:kobv:11-110-18452/28182-7
URL:
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