Self-supervised image quality assessment for X-ray tomographic images of Li-ion battery

Image perception plays a fundamental role in the tomography-based approaches for microstructure characterization and has a deep impact on all subsequent stages of image processing, such as segmentation and 3D analysis. The enhancement of image perception, however, frequently involves observer-depend...

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Bibliographic Details
Published in:npj computational materials 2022-09, Vol.8 (1), p.1-9, Article 194
Main Authors: Zhang, Kai, Nguyen, Tuan-Tu, Su, Zeliang, Demortière, Arnaud
Format: Article
Language:English
Subjects:
639/301/299/891
639/301/930/2735
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Artificial neural networks
Brief Communication
Characterization and Evaluation of Materials
Chemical Sciences
Chemistry and Materials Science
Computational Intelligence
Deep learning
Depth perception
Evaluation
Image enhancement
Image processing
Image quality
Image segmentation
Lithium-ion batteries
Material chemistry
Materials Science
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Methods
Neural networks
Perception
Quality assessment
Quality control
Rechargeable batteries
Theoretical
Tomography
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Summary:Image perception plays a fundamental role in the tomography-based approaches for microstructure characterization and has a deep impact on all subsequent stages of image processing, such as segmentation and 3D analysis. The enhancement of image perception, however, frequently involves observer-dependence, which reflects user-to-user dispersion and uncertainties in the calculated parameters. This work presents an objective quantitative method, which uses convolutional neural networks (CNN) for the quality assessment of the X-ray tomographic images. With only dozens of annotations, our method allows to evaluate directly and precisely the quality of tomographic images. Different metrics were employed to evaluate the correlation between our predicted scores and subjective human annotations. The evaluation results demonstrate that our method can be a direct tool to guide the enhancement process in order to produce reliable segmentation results. The processing of the tomographic image can thus evolve into a robust observer-independent procedure and advance towards the development of an efficient self-supervised approach.
ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-022-00870-z