Nicholls, Daniel 
ORCID: 0000-0003-1677-701X, Robinson, Alex ORCID: 0000-0002-1901-2509, Wells, Jack, Moshtaghpour, Amirafshar ORCID: 0000-0002-6751-2698, Bahri, Mounib ORCID: 0000-0002-8336-9158, Kirkland, Angus and Browning, Nigel ORCID: 0000-0003-0491-251X
(2021)
Compressive Scanning Transmission Electron Microscopy.
[Preprint]
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2112.11955v1.pdf - Submitted version Download (3MB) | Preview |
Abstract
Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quantify the nanoscale atomic structure and composition of materials and biological specimens. In many cases, however, the resolution is limited by the electron beam damage, since in traditional STEM, a focused electron beam scans every location of the sample in a raster fashion. In this paper, we propose a scanning method based on the theory of Compressive Sensing (CS) and subsampling the electron probe locations using a line hop sampling scheme that significantly reduces the electron beam damage. We experimentally validate the feasibility of the proposed method by acquiring real CS-STEM data, and recovering images using a Bayesian dictionary learning approach. We support the proposed method by applying a series of masks to fully-sampled STEM data to simulate the expectation of real CS-STEM. Finally, we perform the real data experimental series using a constrained-dose budget to limit the impact of electron dose upon the results, by ensuring that the total electron count remains constant for each image.
| Item Type: | Preprint |
|---|---|
| Additional Information: | submitted to ICASSP 2022 |
| Uncontrolled Keywords: | eess.SP, eess.SP |
| Divisions: | Faculty of Science and Engineering > School of Engineering |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 12 Jan 2022 15:30 |
| Last Modified: | 15 Mar 2024 13:43 |
| DOI: | 10.48550/arxiv.2112.11955 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3146037 |
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