TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, ca...

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Bibliographic Details
Published in:Scientific reports 2021-03, Vol.11 (1), p.5386-5386, Article 5386
Main Authors: Lin, Ruoqian, Zhang, Rui, Wang, Chunyang, Yang, Xiao-Qing, Xin, Huolin L.
Format: Article
Language:English
Subjects:
639/766/930/328
639/925/930/2735
Algorithms
Browsing
Deep learning
ENERGY STORAGE
Humanities and Social Sciences
Image processing
imaging techniques
Localization
Machine learning
microscopy
multidisciplinary
Noise reduction
Science
Science (multidisciplinary)
Segmentation
TEM spectro-imaging
Transmission electron microscopy
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Summary:Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-84499-w