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Reconstruction of High-resolution Magnetic Field from Sparse Measurements Based on A Super-Resolution Neural Network
ID：93 View Protection：ATTENDEE Updated Time：2025-11-10 15:15:29 Hits：68 Poster Presentation

Start Time：Pending（Asia/Shanghai）

Duration：Pending

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Abstract
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.
Keywords
super-resolution imaging, magnetic field detection, sparse distribution
Speaker
Jiawei Xu
Mr.s Anhui University

Submission Author
Jiawei Xu Anhui University
Xaioxian Wang Anhui University
Siliang Lu Anhui University
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Important Date

  • Conference Date

    Nov 21

    2025

    to

    Nov 23

    2025

  • Oct 20 2025

    Draft paper submission deadline

  • Dec 08 2025

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
South China University of Technology
Organized By
South China University of Technology
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