使用深度学习的磁性系统的超分辨率
D B Lee1,2, H G Yoon1, S M Park1
1Department of Physics, Kyung Hee University, Seoul, 02447, South Korea.
Scientific reports
|July 17, 2023
概括
我们开发了一个深度神经网络,以提高磁自旋结构图像的分辨率,而不需要高分辨率的示例. 这种方法增强了低分辨率显微镜数据,揭示了磁系统中更细微的细节.
科学领域:
- 物理 物理学 物理
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 磁系统会出现自发的对称性破坏,导致复杂的自旋结构.
- 显微镜技术经常产生低分辨率的图像,由于内在的局限性,阻碍了详细的分析.
- 超分辨率成像对于理解纳米级磁现象至关重要.
研究的目的:
- 开发一个深度神经网络 (DNN) 以提高旋转结构图像的分辨率.
- 在培训期间创建一个能够超分辨率的DNN,而不需要地面真实高分辨率图像.
- 为了证明DNN对实验性低分辨率磁性成像数据的适用性.
主要方法:
- 建立了一个深度神经网络,以将低分辨率图像扩展到超高分辨率.
- 该网络使用模拟的磁结构图像进行训练,包括罗迷宫模式和磁域墙壁.
- 通过研究不同数据集的网络性能来评估噪声耐受性和可靠性.
主要成果:
- DNN成功地生成了高分辨率的旋转结构图像,对两个模拟数据集的确切解决方案具有很高的相关性.
- 该网络在不同培训数据类型中展示了对噪声的稳定性和可比可靠性.
- 经过训练的网络有效地应用于磁光克尔效应显微镜和自旋偏振低能电子显微镜的实验数据.
结论:
- 开发的DNN为旋转结构图像的超分辨率提供了一种可行的方法,特别是在没有高分辨率地面真相数据的情况下.
- 这种方法显著提高了从低分辨率实验显微镜分析磁结构.
- 这项研究强调了无监督深度学习在推进磁成像技术方面的潜力.
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