改进相干反斯托克斯拉曼散射显微镜的深度学习最新进展(激光光子学报 18(11)/2024)

IF 9.8 1区 物理与天体物理 Q1 OPTICS
Bowen Yao, Fangrui Lin, Ziyi Luo, Qinglin Chen, Danying Lin, Zhigang Yang, Jia Li, Junle Qu
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引用次数: 0

摘要

深度学习在改进相干反斯托克斯拉曼散射显微技术方面的最新进展相干反斯托克斯拉曼散射(CARS)显微技术可以在获得拉曼光谱信息的同时实现无标记成像,而深度学习在去除非共振背景、用于筛查和诊断的分类以及通过去噪平衡成像速度和图像质量等方面为 CARS 提供了前所未有的支持。有关如何利用深度学习推进 CARS 研究的最新进展,请参阅姚博文、林芳瑞、李佳、曲俊乐及合作者撰写的文章(文章编号 2400562)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Recent Progress in Deep Learning for Improving Coherent Anti-Stokes Raman Scattering Microscopy (Laser Photonics Rev. 18(11)/2024)

Recent Progress in Deep Learning for Improving Coherent Anti-Stokes Raman Scattering Microscopy (Laser Photonics Rev. 18(11)/2024)

Recent Progress in Deep Learning for Improving Coherent Anti-Stokes Raman Scattering Microscopy

Coherent anti-Stokes Raman scattering (CARS) microscopy can obtain Raman spectral information while achieve label-free imaging, and deep learning has provided unprecedented support for CARS in non-resonant background removal, classification for screening and diagnosis, and balancing the imaging speed against the image quality by denoising. For a review of recent progress in how deep learning is utilized to advance CARS studies, see article number 2400562 by Bowen Yao, Fangrui Lin, Jia Li, Junle Qu, and co-workers.

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来源期刊
CiteScore
14.20
自引率
5.50%
发文量
314
审稿时长
2 months
期刊介绍: Laser & Photonics Reviews is a reputable journal that publishes high-quality Reviews, original Research Articles, and Perspectives in the field of photonics and optics. It covers both theoretical and experimental aspects, including recent groundbreaking research, specific advancements, and innovative applications. As evidence of its impact and recognition, Laser & Photonics Reviews boasts a remarkable 2022 Impact Factor of 11.0, according to the Journal Citation Reports from Clarivate Analytics (2023). Moreover, it holds impressive rankings in the InCites Journal Citation Reports: in 2021, it was ranked 6th out of 101 in the field of Optics, 15th out of 161 in Applied Physics, and 12th out of 69 in Condensed Matter Physics. The journal uses the ISSN numbers 1863-8880 for print and 1863-8899 for online publications.
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