Compressed ultrafast transmission electron microscopy: a simulation study

Xianglei Liu; Shian Zhang; Aycan Yurtsever; Jinyang Liang

doi:10.1117/12.2510394

21 February 2019 Compressed ultrafast transmission electron microscopy: a simulation study

Xianglei Liu, Shian Zhang, Aycan Yurtsever, Jinyang Liang

Author Affiliations +

Proceedings Volume 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI; 108831F (2019) https://doi.org/10.1117/12.2510394
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

Bringing ultrafast temporal resolution to transmission electron microscopy (TEM) has historically been challenging. Despite significant recent progress in this direction, it remains difficult to achieve sub-nanosecond temporal resolution with a single electron pulse imaging. To address this limitation, here, we propose a methodology that combines laserassisted TEM with computational imaging methodologies based on compressed sensing (CS). In this technique, a twodimensional (2D) transient event [i.e. (x, y) frames that vary in time] is recorded through a CS paradigm. The 2D streak image generated on a camera is used to reconstruct the datacube of the ultrafast event, with two spatial and one temporal dimensions, via a CS-based image reconstruction algorithm. Using numerical simulation, we find that the reconstructed results are in good agreement with the ground truth, which demonstrates the applicability of CS-based computational imaging methodologies to laser-assisted TEM. Our proposed method, complementing the existing ultrafast stroboscopic and nanosecond single-shot techniques, opens up the possibility for single-shot, spatiotemporal imaging of irreversible structural phenomena with sub-nanosecond temporal resolution.

Citation Download Citation

Xianglei Liu, Shian Zhang, Aycan Yurtsever, and Jinyang Liang "Compressed ultrafast transmission electron microscopy: a simulation study", Proc. SPIE 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI, 108831F (21 February 2019); https://doi.org/10.1117/12.2510394

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