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TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Crossref DOI link: https://doi.org/10.1038/s41598-021-84499-w

Published Online: 2021-03-08

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Lin, Ruoqian

Zhang, Rui

Wang, Chunyang

Yang, Xiao-Qing

Xin, Huolin L.
Funding

Funding for this research was provided by:

U.S. Department of Energy (DE-SC0012704, DE-SC0021204)

University of California, Irvine (Startup)
License Information

Text and Data Mining valid from 2021-03-08

Version of Record valid from 2021-03-08
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Article History

Received: 8 August 2020

Accepted: 10 February 2021

First Online: 8 March 2021

Competing interests

: The authors declare no competing interests.

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