Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-23T14:35:59.829Z Has data issue: false hasContentIssue false
  • English
  • Français

Imaging of Defect Rich Heterogeneous Interfaces using Compressive Sensing STEM

Published online by Cambridge University Press:  22 July 2022

Daniel Nicholls ,
Jack Wells ,
Mounib Bahri  and
Nigel D. Browning
Daniel Nicholls*
Affiliation:
Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, United Kingdom
Jack Wells
Affiliation:
Distributed Algorithms Centre for Doctoral Training, University of Liverpool, Liverpool, L69 3GH, United Kingdom
Mounib Bahri
Affiliation:
Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, United Kingdom
Nigel D. Browning
Affiliation:
Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, United Kingdom Physical and Computational Science Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA Sivananthan Laboratories, 590 Territorial Drive, Bolingbrook, IL 60440. USA The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot OX11 0RA, United Kingdom
*
*Corresponding author: [email protected]

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Advanced Imaging and Spectroscopy for Nanoscale Materials
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 2488 - 2489
Check for updates
Copyright
Copyright © Microscopy Society of America 2022

References

Stevens, A., et al. , Applying compressive sensing to TEM video: a substantial frame rate increase on any camera. Advanced Structural and Chemical Imaging, 2015. 1(1).CrossRefGoogle Scholar
Stevens, A., et al. , The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images. Microscopy (Oxf), 2014. 63(1): p. 41-51.CrossRefGoogle ScholarPubMed
Nicholls, D., et al. , Sub-Sampled Imaging for STEM: Maximising Image Speed, Resolution and Precision Through Reconstruction Parameter Refinement. Submitted, 2021.CrossRefGoogle Scholar
Sertoglu, S. and Paisley, J., Scalabe Bayesian nonparametric dictionary learning. 23rd European Signal Processing Conference (EUSIPCO), 2015: p. 2771-2775.CrossRefGoogle Scholar
Berding, M., Native defects in CdTe. Physical Review B, 1999. 60(12): p. 8943.Google Scholar