Super-Resolution Reconstruction of Transmission Electron Microscopy Images Using Deep Learning

Suveer, Amit; Gupta, Anindya; Kylberg, Gustaf; Sintorn, Ida‐Maria

doi:10.1109/isbi.2019.8759153

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…2 GAN) [22][23][24] or case-independent denoising models that successfully outperformed classical restoration filters for both TEM and STEM. 23,[25][26][27][28][29][30][31] Interestingly J. Vincent et al studied the latent features learned by the DL model to unveil the nature of the trained denoising dependencies to shine light on what is typically left as a black box. They showed that the FCNN learns to adapt its filtering strategies depending on the structural properties of every particular region in the image.…”

Section: Electron Microscopy Advances With Machine Learningmentioning

confidence: 99%

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

2022

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Section: Electron Microscopy Advances With Machine Learningmentioning

confidence: 99%

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

2022

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“…Given the data-intensive nature of TEM imagery, there have been recent efforts to employ CNNs in the analysis of TEM images. Examples of using neural networks for analyzing TEM images include using CNNs for denoising TEM images [6,7], generating TEM images from partial scans [8], enhancing TEM images [9,10], classifying types of crystalline structures [11], locating defects in non-crystalline materials [12], mapping atomic structures and defects [1], and mapping general structures of interest [2].…”

Section: Related Workmentioning

confidence: 99%

Defect Detection in Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning

et al. 2021

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Point defects play a fundamental role in the discovery of new materials due to their strong influence on material properties and behavior. At present, imaging techniques based on transmission electron microscopy (TEM) are widely employed for characterizing point defects in materials. However, current methods for defect detection predominantly involve visual inspection of TEM images, which is laborious and poses difficulties in materials where defect related contrast is weak or ambiguous. Recent efforts to develop machine learning methods for the detection of point defects in TEM images have focused on supervised methods that require labeled training data that is generated via simulation. Motivated by a desire for machine learning methods that can be trained on experimental data, we propose two self-supervised machine learning algorithms that are trained solely on images that are defect-free. Our proposed methods use principal components analysis (PCA) and convolutional neural networks (CNN) to analyze a TEM image and predict the location of a defect. Using simulated TEM images, we show that PCA can be used to accurately locate point defects in the case where there is no imaging noise. In the case where there is imaging noise, we show that incorporating a CNN dramatically improves model performance. Our models rely on a novel approach that uses the residual between a TEM image and its PCA reconstruction.

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“…Deep learning can leverage an understanding of physics to infill images [256][257][258] . Example applications include increasing SEM 178,259,260 , STEM 201,261 and TEM 262 resolution, and infilling continuous sparse scans 200 . Example applications of DNNs to complete sparse spiral and grid scans are shown in figure 2.…”

Section: Compressed Sensingmentioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

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Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

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Super-Resolution Reconstruction of Transmission Electron Microscopy Images Using Deep Learning

Cited by 13 publications

References 8 publications

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

Defect Detection in Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning

Review: Deep Learning in Electron Microscopy

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