Hostname: page-component-7c8c6479df-94d59 Total loading time: 0 Render date: 2024-03-29T00:43:07.967Z Has data issue: false hasContentIssue false

AtomSegNet and TomoFillNet—Two Deep Learning Open-Source Apps for Superresolution Processing of Atomic Resolution Images and Missing-wedge Information Inpainting in Electron Tomograms

Published online by Cambridge University Press:  30 July 2020

Huolin Xin
Affiliation:
University of California-Irvine, Irvine, California, United States
Ruoqian Lin
Affiliation:
Brookhaven National Laboratory, Upton, New York, United States
Rui Zhang
Affiliation:
University of California-Irvine, Irvine, California, United States
Chunyang Wang
Affiliation:
University of California-Irvine, Irvine, California, United States
Guanglei Ding
Affiliation:
University of California-Irvine, Irvine, California, United States
He Wei
Affiliation:
University of California-Irvine, Irvine, California, United States

Abstract

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Copyright
Copyright © Microscopy Society of America 2020

References

Ding, G., Liu, Y., Zhang, R. & Xin, H. L. A joint deep learning model to recover information and reduce artifacts in missing-wedge sinograms for electron tomography and beyond. Scientific reports 9, 12803, doi:10.1038/s41598-019-49267-x (2019).CrossRefGoogle ScholarPubMed