Compression of Battery X-Ray Tomography Data with Machine Learning

被引：0

作者：

颜子沛 ^{[1
]}

王其钰 ^{[2
,3
,4
]}

禹习谦 ^{[2
,3
,4
]}

李济舟 ^{[1
,5
]}

吴国宝 ^{[6
]}

机构：

[1] Department of Electronic Engineering, The Chinese University of Hong Kong

[2] Beijing National Laboratory for Condensed Matter Physics, and Institute of Physics,Chinese Academy of Sciences

[3] Huairou Division, Institute of Physics, Chinese Academy of Sciences

[4] Center of Materials Science and Optoelectronics Engineering,University of Chinese Academy of Sciences

[5] CUHK Shenzhen Research Institute

[6] Department of Mathematics, Hong Kong Baptist

来源：

Chinese Physics Letters | 2024年 / 41卷 / 09期

关键词：

D O I：

暂无

中图分类号：

学科分类号：

摘要：

<正>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.

引用

页码：110 / 115

页数：6

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