Physics constrained unsupervised deep learning for rapid, high resolution scanning coherent diffraction reconstruction
Abstract By circumventing the resolution limitations of optics, coherent diffractive imaging (CDI) and ptychography are making their way into scientific fields ranging from X-ray imaging to astronomy. Yet, the need for time consuming iterative phase recovery hampers real-time imaging. While supervis...
Main Authors: | Oliver Hoidn, Aashwin Ananda Mishra, Apurva Mehta |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-12-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-023-48351-7 |
Similar Items
-
Uncertainty quantification for deep learning in particle accelerator applications
by: Aashwin Ananda Mishra, et al.
Published: (2021-11-01) -
AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging
by: Yudong Yao, et al.
Published: (2022-06-01) -
Low‐coherent optical diffraction tomography by angle‐scanning illumination
by: Lee, KyeoReh, et al.
Published: (2021) -
High Resolution Powder Electron Diffraction in Scanning Electron Microscopy
by: Miroslav Slouf, et al.
Published: (2021-12-01) -
Mapping nanocrystal orientations via scanning Laue diffraction microscopy for multi-peak Bragg coherent diffraction imaging
by: Yueheng Zhang, et al.
Published: (2023-07-01)