Deep reinforcement learning for data-driven adaptive scanning in ptychography

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 trai...

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Detalles Bibliográficos
Autores principales: Schloz, Marcel, Müller, Johannes, Pekin, Thomas C., Van den Broek, Wouter, Madsen, Jacob, Susi, Toma, Koch, Christoph T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229550/
https://www.ncbi.nlm.nih.gov/pubmed/37253763
http://dx.doi.org/10.1038/s41598-023-35740-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229550/
https://www.ncbi.nlm.nih.gov/pubmed/37253763
http://dx.doi.org/10.1038/s41598-023-35740-1

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