| CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
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Compression of Battery X-Ray Tomography Data with Machine Learning |
| Zipei Yan1, Qiyu Wang2,3,4, Xiqian Yu2,3,4*, Jizhou Li1,5*, and Michael K.-P. Ng6 |
1Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
2Beijing National Laboratory for Condensed Matter Physics, and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
3Huairou Division, Institute of Physics, Chinese Academy of Sciences, Beijing 101400, China
4Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
5CUHK Shenzhen Research Institute, Shenzhen 518057, China
6Department of Mathematics, Hong Kong Baptist University, Hong Kong, China
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| Cite this article: |
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Zipei Yan, Qiyu Wang, Xiqian Yu et al 2024 Chin. Phys. Lett. 41 098901 |
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Abstract With the increasing demand for high-resolution x-ray tomography in battery characterization, the challenges of storing, transmitting, and analyzing substantial imaging data necessitate more efficient solutions. Traditional data compression methods struggle to balance reduction ratio and image quality, often failing to preserve critical details for accurate analysis. This study proposes a machine learning-assisted compression method tailored for battery x-ray imaging data. Leveraging physics-informed representation learning, our approach significantly reduces file sizes without sacrificing meaningful information. We validate the method on typical battery materials and different x-ray imaging techniques, demonstrating its effectiveness in preserving structural and chemical details. Experimental results show an up-to-95 compression ratio while maintaining high fidelity in the projection and reconstructed images. The proposed framework provides a promising solution for managing large-scale battery x-ray imaging datasets, facilitating significant advancements in battery research and development.
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Received: 17 June 2024
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Published: 19 September 2024
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