Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks
For a deeper understanding of the functional behavior of energy materials, it is necessary to investigate their microstructure, e.g., via imaging techniques like scanning electron microscopy (SEM). However, active materials are often heterogeneous, necessitating quantification of features over large...
Main Authors: | Finegan, D.P (Author), Furat, O. (Author), Kirstein, T. (Author), Schmidt, V. (Author), Smith, K. (Author), Yang, Z. (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Research
2022
|
Subjects: | |
Online Access: | View Fulltext in Publisher |
Similar Items
-
Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy
by: Md. Shahinur Alam, et al.
Published: (2021-03-01) -
Generative adversarial networks for single image super resolution in microscopy images
by: Gawande, Saurabh
Published: (2018) -
White-Light Interference Microscopy Image Super-Resolution Using Generative Adversarial Networks
by: Haowei Li, et al.
Published: (2020-01-01) -
Tip Crack Imaging on Transparent Materials by Digital Holographic Microscopy
by: Wen-Jing Zhou, et al.
Published: (2019-10-01) -
Graphene/PVDF Composites for Ni-rich Oxide Cathodes Toward High-Energy Density Li-ion Batteries
by: Chang Won Park, et al.
Published: (2021-04-01)