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Reconstruction of High-resolution Magnetic Field from Sparse Measurements Based on A Super-Resolution Neural Network
编号：93 访问权限：仅限参会人 更新：2025-11-10 15:15:29 浏览：9次 张贴报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

所在会场：[暂无会议] [暂无会议段]

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摘要
This paper proposes a super-resolution reconstruction network designed to recover complete magnetic field distributions from sparse magnetic field measurements. The network incorporates two down-sampling convolutional modules after the input layer, enabling it to directly process sparse high-resolution magnetic field data and reconstruct comprehensive magnetic field maps. To evaluate the model’s performance, a training dataset was generated using simulated magnetic field images, while real magnetic field images were collected through a custom-built experimental setup for testing. Experimental results demonstrate that the proposed method achieves effective reconstruction in both simulated and real-world scenarios, validating its accuracy and practical applicability.
关键词
super-resolution imaging, magnetic field detection, sparse distribution
报告人
Jiawei Xu
Mr.s Anhui University

稿件作者
Jiawei Xu Anhui University
Xaioxian Wang Anhui University
Siliang Lu Anhui University
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重要日期

  • 会议日期

    11月21日

    2025

    至

    11月23日

    2025

  • 10月20日 2025

    初稿截稿日期

  • 11月23日 2025

    注册截止日期

主办单位
IEEE Instrumentation and Measurement Society
South China University of Technology
承办单位
South China University of Technology
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