Journal article published in Chin. Phys. Lett.
Zipei Yan, Qiyu Wang, Xiqian Yu, Jizhou Li, and Michael K.-P. Ng. “Compression of Battery X-Ray Tomography Data with Machine Learning”, Chin. Phys. Lett. 2024, 41 (9): 098901, DOI: 10.1088/0256-307X/41/9/098901.
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.
Key Features
- Machine Learning-assisted Compression: The proposed ZIP model to learn and compress intricate patterns in x-ray images.
- High Compression Ratio: Achieves up to 95x compression ratio without sacrificing meaningful information.
- Wide Applicability: Effective on various x-ray imaging techniques and battery materials.
- Robust Performance: Maintains high fidelity in projection and reconstructed images, preserving structural and chemical details.
Code
The Pytorch implementation is available at the GitHub repository: https://github.com/TISGroup/ZIP.
