Framework of compressive sensing and data compression for 4D-STEM Article Swipe

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Hsu-Chih Ni , Renliang Yuan , Jiong Zhang , Jian‐Min Zuo ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2308.05645

Four-dimensional Scanning Transmission Electron Microscopy (4D-STEM) is a powerful technique for high-resolution and high-precision materials characterization at multiple length scales, including the characterization of beam-sensitive materials. However, the field of view of 4D-STEM is relatively small, which in absence of live processing is limited by the data size required for storage. Furthermore, the rectilinear scan approach currently employed in 4D-STEM places a resolution- and signal-dependent dose limit for the study of beam sensitive materials. Improving 4D-STEM data and dose efficiency, by keeping the data size manageable while limiting the amount of electron dose, is thus critical for broader applications. Here we develop a general method for reconstructing 4D-STEM data with subsampling in both real and reciprocal spaces at high fidelity. The approach is first tested on the subsampled datasets created from a full 4D-STEM dataset, and then demonstrated experimentally using random scan in real-space. The same reconstruction algorithm can also be used for compression of 4D-STEM datasets, leading to a large reduction (100 times or more) in data size, while retaining the fine features of 4D-STEM imaging, for crystalline samples.

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Scanning Transmission Electron Microscopy

Transmission Electron Microscopy

Remote Sensing

Engineering

Biology

Mechanical Engineering

Programming Language

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Limiting Scanning transmission electron microscopy Characterization (materials science) High fidelity High resolution Stem cell Computer science Materials science SIGNAL (programming language) Optics Physics Nanotechnology Acoustics Transmission electron microscopy Remote sensing Engineering Biology Mechanical engineering Programming language Geology Genetics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2308.05645
PDF: https://arxiv.org/pdf/2308.05645
OA Status: green
Cited By: 1
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2308.05645

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Title: Framework of compressive sensing and data compression for 4D-STEM

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-08-10

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Authors: Hsu-Chih Ni, Renliang Yuan, Jiong Zhang, Jian‐Min Zuo

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Landing page: https://arxiv.org/abs/2308.05645

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Concepts: Limiting, Scanning transmission electron microscopy, Characterization (materials science), High fidelity, High resolution, Stem cell, Computer science, Materials science, SIGNAL (programming language), Optics, Physics, Nanotechnology, Acoustics, Transmission electron microscopy, Remote sensing, Engineering, Biology, Mechanical engineering, Programming language, Geology, Genetics

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Cited by: 1

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abstract_inverted_index.real-space.	143
abstract_inverted_index.rectilinear	53
abstract_inverted_index.resolution-	62
abstract_inverted_index.subsampling	110
abstract_inverted_index.Furthermore,	51
abstract_inverted_index.Transmission	2
abstract_inverted_index.demonstrated	137
abstract_inverted_index.applications.	98
abstract_inverted_index.beam-sensitive	24
abstract_inverted_index.experimentally	138
abstract_inverted_index.high-precision	13
abstract_inverted_index.reconstructing	106
abstract_inverted_index.reconstruction	146
abstract_inverted_index.high-resolution	11
abstract_inverted_index.Four-dimensional	0
abstract_inverted_index.characterization	15, 22
abstract_inverted_index.signal-dependent	64
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	4
citation_normalized_percentile