Sub-Sampled Imaging for STEM: Maximising Image Speed, Resolution and Precision Through Reconstruction Parameter Refinement

Nicholls, Daniel; Wells, Jack; Stevens, Andrew; Zheng, Yalin; Castagna, Jony; Browning, Nigel D.

doi:10.1016/j.ultramic.2021.113451

Cited by 23 publications

(21 citation statements)

References 52 publications

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“…In recent years, various Compressive Sensing (CS) approaches have been applied to Scanning Transmission Electron Microscopy (STEM) images [1][2][3], demonstrating that subsampled image acquisition, accompanied by an appropriate method of inpainting the missing pixel information, provides an alternative method for achieving low-dose STEM and imaging beam-sensitive materials [4]. A full image inpainting algorithm (taking only a subsampled measurement as its input) typically consists of a blind dictionary learning algorithm such as Beta-Process Factor Analysis (BPFA) (developed in [5]) which learns the representative patterns within the target image, followed by a sparse-coding algorithm which aims to find the optimum combination of the learned dictionary 'elements' to best represent each overlapping patch of the image provided in the target batch.…”

mentioning

confidence: 99%

Approaching Real-Time Low-Dose STEM: Image Recovery from Subsampled Measurements via Online Bayesian Dictionary Learning

Wells

Nicholls

Robinson

et al. 2022

Microscopy and Microanalysis

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mentioning

confidence: 99%

Approaching Real-Time Low-Dose STEM: Image Recovery from Subsampled Measurements via Online Bayesian Dictionary Learning

Wells

Nicholls

Robinson

et al. 2022

Microscopy and Microanalysis

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“…In practice, this is achieved by minimizing the experimental dose, dose rate and dose overlap for any image, resulting in a controlled dose fractionation that maximizes the data content per unit dose, i.e. reducing the number of pixels being sampled (Figure 1), and using inpainting /machine learning methods to reconstruct the images [1,2].…”

mentioning

confidence: 99%

“…At some point (f) the level of subsampling leads to an unacceptable error in the reconstruction. This level of sub-sampling is material and instrument dependent[2].…”

mentioning

confidence: 99%

Efficient Sampling and Reconstruction Strategies for in-situ SEM/STEM

Browning

Bahri

Castagna

et al. 2022

Microscopy and Microanalysis

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“…Compressive sensing is another emerging technique that explores alternatives scanning algorithms to overcome an issue of oversampled images at high magnification [149][150][151][152][153][154].…”

Section: Preventive Actionsmentioning

confidence: 99%

“…The total electron dose and dose rate can be reduced by acquiring STEM images with reduced number of pixels than used to acquire regular raster imaging. Full images can be reconstructed using mathematical algorithms [151,155]. This method allows better control of the dose rate and dose overlap delivered to the sample.…”

Section: Preventive Actionsmentioning

confidence: 99%

Revealing nanoscale lithiation and dissolution pathways by in situ cryogenic electron microscopy

Tiukalova¹

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Materials for energy storage and conversion devices are of high importance to meet future industrial energy demands. Detailed understanding of chemical processes occurring at the solid/liquid interfaces and alterations of the structure at the atomic scale is critical for the design and development of future devices. This Ph.D. work focuses on two central systems of particular relevance for energy applications, i.e., LiNi0.5Mn1.5O4 Li-ion battery cathode material and two catalysts (La2LiIrO6, ZnCo1.2Ni0.8O4) for the oxygen evolution reaction (OER) applications. Through a deep understanding of the evolution of the structure at the local atomic scale, the overall aim is to get insights into the atomic structure-property relationship to improve our understanding and provide information that will help develop materials capable of higher capacity, longer lifetime, and overall better performance.During operation, electrochemical reactions introduce structural and chemical changes to the active materials that are not necessarily reversible. These alterations cause degradation of the device performance and limit their lifetime. A detailed understanding of the correlation between electrochemical processes and atomic-scale transformations is essential to improve the performance of the devices. Owing to its high-resolution transmission electron microscopy (TEM) -based imaging and spectroscopic analysis is able to provide unique information that is not available with other methods. In addition, with the recent development of dedicated in situ and operando TEM-capabilities, it is possible to observe structural changes under the application of external stimuli such as at cryogenic temperature or presence of a liquid electrolyte, allowing to study dynamical processes in their native operation environment. The development of in situ cryogenic temperature stages for materials science applications has opened up many new possibilities, including learning new information about beam-sensitive materials that are challenging to characterize with conventional room temperature imaging at the atomic level. This Ph.D. work focuses on three material systems studied in detail under various HR(S)TEM and spectroscopy methods: LiNi0.5Mn1.5O4 as a promising cathode material, ZnCo1.2Ni0.8O4 and La2LiIrO6 as catalysts for OER. It was found that LiNi0.5Mn1.5O4

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Sub-Sampled Imaging for STEM: Maximising Image Speed, Resolution and Precision Through Reconstruction Parameter Refinement

Cited by 23 publications

References 52 publications

Approaching Real-Time Low-Dose STEM: Image Recovery from Subsampled Measurements via Online Bayesian Dictionary Learning

Approaching Real-Time Low-Dose STEM: Image Recovery from Subsampled Measurements via Online Bayesian Dictionary Learning

Efficient Sampling and Reconstruction Strategies for in-situ SEM/STEM

Revealing nanoscale lithiation and dissolution pathways by in situ cryogenic electron microscopy

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