数据驱动的信号对噪声的增强在散射近场红外显微镜中
Carlos R Baiz1,2, Katerina Kanevche2,3, Jacek Kozuch2
1Department of Chemistry, University of Texas at Austin, 105 E 24th St. A5300, Austin, Texas 78712, USA.
The Journal of chemical physics
|February 3, 2025
概括
机器学习通过消除噪声来增强散射类型扫描近场光学显微镜 (s-SNOM) 图像,显著改善信号噪声比. 这种方法使得高分辨率成像更快,更容易用于各种科学应用.
科学领域:
- 光学显微镜的使用方法
- 机器学习 机器学习
- 图像处理 图像处理
背景情况:
- 散射式扫描近场光学显微镜 (s-SNOM) 提供高空间分辨率,但信号噪声比较低,特别是对于吸收较弱的样品.
- 低信号水平限制了s-SNOM在详细纳米分析中的有效性和适用性.
研究的目的:
- 开发一种基于机器学习的方法来消除s-SNOM图像的噪声,并提高信号与噪声的比率.
- 提高高分辨率s-SNOM成像的质量和可行性,特别是对于具有挑战性的样品.
主要方法:
- 使用生成对抗神经网络 (CycleGANs) 的数据驱动,基于补丁的机器学习重建方法被用于图像解密.
- 循环GAN模型在不同采集时间采集的s-SNOM图像的未配对数据集上进行训练,以学习噪声特征.
- 该方法旨在灵活重建任意大小的图像,以适应可变的扫描样本区域.
主要成果:
- 机器学习方法显著提高了s-SNOM的图像质量,由结构相似度指数和峰值信号与噪声比率的增加证明.
- 增强的图像质量与实现整合时间的四倍可比,这表明噪音减少显著.
- 该方法成功地保存了重要的地形和分子信息,同时有效地建模和消除仪器噪声.
结论:
- 开发的机器学习方法有效地消除了s-SNOM图像,大大提高了信号噪声比和图像质量.
- 这种技术有可能减少数据采集时间,使先进的s-SNOM分析变得更加实用.
- 这种方法扩大了高分辨率s-SNOM的可行性,用于各种生物和材料科学研究.
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