{"title":"Recent Progress in Deep Learning for Improving Coherent Anti-Stokes Raman Scattering Microscopy (Laser Photonics Rev. 18(11)/2024)","authors":"Bowen Yao, Fangrui Lin, Ziyi Luo, Qinglin Chen, Danying Lin, Zhigang Yang, Jia Li, Junle Qu","doi":"10.1002/lpor.202470064","DOIUrl":null,"url":null,"abstract":"<p><b>Recent Progress in Deep Learning for Improving Coherent Anti-Stokes Raman Scattering Microscopy</b></p><p>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.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":204,"journal":{"name":"Laser & Photonics Reviews","volume":"18 11","pages":""},"PeriodicalIF":9.8000,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lpor.202470064","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Laser & Photonics Reviews","FirstCategoryId":"101","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/lpor.202470064","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.