高对比度,高分辨率和快速超光谱显微镜和纳米显微镜仪器的噪声学习
Hao He1,2,3, Maofeng Cao2, Yun Gao1
1Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China.
Nature communications
|January 25, 2024
概括
一种新的噪声学习 (NL) 方法通过学习和消除仪器噪声,在拉曼光谱学中显著提高了信号噪声比 (SNR). 这种方法可以提高成像速度,分辨率和准确性,而不需要大量的数据标签.
科学领域:
- 频谱学是一种光谱学.
- 纳米成像技术的使用
- 数据科学数据科学数据科学
背景情况:
- 拉曼散射效率低,限制了信号噪声比 (SNR),成像速度和分辨率.
- 在光谱成像中同时实现高SNR,速度和分辨率是一个重大挑战.
研究的目的:
- 开发一种噪声学习 (NL) 方法来增强SNR并克服拉曼纳米成像中的局限性.
- 为了提高定位精度,空间分辨率,并减少热漂移效应在尖端增强的拉曼光谱纳米成像.
主要方法:
- 噪声学习 (NL) 方法在像素空间频率域中统计学学习内在仪器噪声.
- 从光谱中删除估计的噪声,增强SNR并减少平均平方误差.
- 该方法将地面真相光谱和仪器噪声集成到训练数据集中,避免了大量的手动标签.
主要成果:
- NL方法将SNR提高约10倍,并将平均平方误差减少近150倍.
- 通过尖端增强的拉曼光谱纳米成像实现了更好的定位精度和空间分辨率.
- 热漂移对纳米成像的影响在很大程度上被消除了.
结论:
- 噪音学习 (NL) 方法有效地提高了Raman光谱中的SNR和图像质量.
- NL适用于其他成像技术,如光和光发光.
- 这种依赖仪器的深度学习方法绕过了对大型,样本特定的标记数据集的需求.
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