基于剩余网络自适应聚焦的拉曼信号优化
Haozhao Chen1, Liwei Yang2, Weile Zhu1
1Key Laboratory of Photonic Technology for Integrated Sensing and Communication, Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|January 26, 2024
概括
本研究介绍了一种快速,准确的适应性聚焦方法,用于使用残余网络的微拉曼光谱. 它提高了光谱数据的质量和灵敏度,这对于复杂环境中的分子识别至关重要.
科学领域:
- 频谱学是一种光谱学.
- 生物技术是生物技术.
- 机器学习 机器学习
背景情况:
- 微拉曼光谱为分子分析提供高灵敏度和特异性.
- 精确的样本聚焦对于高质量的微拉曼信号至关重要,特别是在像细胞内环境这样的复杂环境中.
- 传统的自动对焦方法很慢,可能需要额外的硬件,阻碍实时应用程序.
研究的目的:
- 为微拉曼光谱学开发一种快速而准确的适应性聚焦方法.
- 提高微拉曼光谱数据采集的质量和可靠性.
主要方法:
- 开发了一种使用残余网络 (Resnet50) 的自适应聚焦方法.
- 该方法使用明亮场图像,通过结合梯度和离散等号变换来预测失焦距离.
- 明亮场地图的区域划分表征了样本表面高度变化.
主要成果:
- 开发的方法可以从单个明亮场图像中在120ms内达到1μm的焦点预测准确度.
- 证明了微拉曼信号的成功优化和光谱信息的校正.
- 该技术可以在没有额外的硬件或漫长的处理时间的情况下实现准确的聚焦.
结论:
- 拟议的自适应聚焦方法显著提高了微拉曼光谱的灵敏度和精度.
- 这一进展对于促进微拉曼光谱在各种科学领域的广泛应用至关重要.
- 基于残余网络的方法为实时微拉曼测量提供了兼容和高效的解决方案.
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