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Super-resolution of magnetic systems using deep learning.

D B Lee1,2, H G Yoon1, S M Park1

  • 1Department of Physics, Kyung Hee University, Seoul, 02447, South Korea.

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Summary
This summary is machine-generated.

We developed a deep neural network to improve the resolution of magnetic spin structure images without needing high-resolution examples. This method enhances low-resolution microscopy data, revealing finer details in magnetic systems.

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Area of Science:

  • Physics
  • Materials Science
  • Computer Science

Background:

  • Magnetic systems exhibit spontaneous symmetry breaking, leading to complex spin structures.
  • Microscopy techniques often produce low-resolution images due to intrinsic limitations, hindering detailed analysis.
  • Super-resolution imaging is crucial for understanding nanoscale magnetic phenomena.

Purpose of the Study:

  • To develop a deep neural network (DNN) for enhancing the resolution of spin structure images.
  • To create a DNN capable of super-resolution without requiring ground truth high-resolution images during training.
  • To demonstrate the DNN's applicability to experimental low-resolution magnetic imaging data.

Main Methods:

  • A deep neural network was constructed to expand low-resolution images to super-resolution.
  • The network was trained using simulated magnetic structure images, including chiral maze patterns and magnetic domain walls.
  • Noise tolerance and reliability were assessed by studying the network's performance with varying datasets.

Main Results:

  • The DNN successfully generated high-resolution spin structure images with high correlation to exact solutions for both simulated datasets.
  • The network demonstrated robustness against noise and comparable reliability across different training data types.
  • The trained network was effectively applied to experimental data from magneto-optical Kerr effect microscopy and spin-polarized low-energy electron microscopy.

Conclusions:

  • The developed DNN provides a viable method for super-resolution of spin structure images, particularly when high-resolution ground truth data is unavailable.
  • This approach significantly enhances the analysis of magnetic structures from low-resolution experimental microscopy.
  • The study highlights the potential of unsupervised deep learning for advancing magnetic imaging techniques.