Challenging point scanning across electron microscopy and optical imaging using computational imaging

Kallepalli, A. et al. (2022) Challenging point scanning across electron microscopy and optical imaging using computational imaging. Intelligent Computing, 2022, 0001. (doi: 10.34133/icomputing.0001)

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Solving challenges of enhanced imaging (resolution or speed) is a continuously changing frontier of research. Within this sphere, ghost imaging (and the closely related single-pixel imaging) has evolved as an alternative to focal plane detector arrays owing to advances in detectors and/or modulation devices. The interest in these techniques is due to their robustness to varied sets of patterns and applicability to a broad range of wavelengths and compatibility with compressive sensing. To achieve a better control of illumination strategies, modulators of many kinds have long been available in the optical regime. However, analogous technology to control of phase and amplitude of electron beams does not exist. We approach this electron microscopy challenge from an optics perspective, with a novel approach to imaging with non-orthogonal pattern sets using ghost imaging. Assessed first in the optical regime and subsequently in electron microscopy, we present a methodology that is applicable at different spectral regions and robust to non-orthogonality. The distributed illumination pattern sets also result in a reduced peak intensity, thereby potentially reducing damage of samples during imaging. This imaging approach is potentially translatable beyond both regimes explored here, as a single-element detector system.

Item Type:Articles
Additional Information:The authors wish to acknowledge support from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 766970 Project “Q-Sort” and no. 964591 “SMART-electron”), the Royal Society EPSRC Research Council funding to QuantIC (EP/M01326X/1), and the National Natural Science Foundation of China (grant no. 61922011 and U21B2034).
Glasgow Author(s) Enlighten ID:Gibson, Dr Graham and Kallepalli, Dr Akhil and Bowman, Mr Richard and Sun, Dr Mingjie and Padgett, Professor Miles and Stellinga, Dr Daan
Authors: Kallepalli, A., Viani, L., Stellinga, D., Rotunno, E., Bowman, R., Gibson, G. M., Sun, M.-J., Rosi, P., Frabboni, S., Balboni, R., Migliori, A., Grillo, V., and Padgett, M. J.
College/School:College of Science and Engineering > School of Physics and Astronomy
Journal Name:Intelligent Computing
Publisher:American Association for the Advancement of Science
ISSN (Online):2771-5892
Published Online:21 December 2022
Copyright Holders:Copyright © 2022 Akhil Kallepalli et al.
First Published:First published in Intelligent Computing 2022: 0001
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
301480Quantum SorterMiles PadgettEuropean Commission (EC)766970P&S - Physics & Astronomy
190841UK Quantum Technology Hub in Enhanced Quantum ImagingMiles PadgettEngineering and Physical Sciences Research Council (EPSRC)EP/M01326X/1P&S - Physics & Astronomy