Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy

Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM’s high affinity with various analytical options into account. As is well known, STEM’s image acquisition time needs to be...

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Detalles Bibliográficos
Autores principales: Ihara, Shiro, Saito, Hikaru, Yoshinaga, Mizumo, Avala, Lavakumar, Murayama, Mitsuhiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356044/
https://www.ncbi.nlm.nih.gov/pubmed/35931705
http://dx.doi.org/10.1038/s41598-022-17360-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356044/
https://www.ncbi.nlm.nih.gov/pubmed/35931705
http://dx.doi.org/10.1038/s41598-022-17360-3

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