深度学习在光纤成像中的应用进展(特邀)  

Advances in Deep Learning Based Fiber Optic Imaging(Invited)

作　　者：孙佳伟 陈照青 赵斌 李学龙[1,3] Sun Jiawei;Chen Zhaoqing;Zhao Bin;Li Xuelong(Intelligent Photonics and Electronics Center(IPEC),Shanghai Artificial Intelligence Laboratory,Shanghai 200232,China;School of Artificial Intelligence,OPtics and ElectroNics(iOPEN),Northwestern Polytechnical University,Xi’an 710072,Shaanxi,China;Institute of Artificial Intelligence(TeleAI),China Telecom Co.Ltd.,Beijing 100033,China)

机构地区：[1]上海人工智能实验室智能光电中心,上海200232 [2]西北工业大学光电与智能研究院,陕西西安710072 [3]中国电信人工智能研究院,北京100033

出　　处：《激光与光电子学进展》2024年第16期60-75,共16页Laser & Optoelectronics Progress

基　　金：国家重点研发计划(2022ZD0160102);国家自然科学基金(62376222);中国科协青年人才托举工程(2023QNRC001)。

摘　　要：光纤成像技术借助光纤的微小尺寸与柔韧性能实现对狭窄区域的高分辨率成像,在生物医学、工业检测等领域都有广泛应用。然而,在基于多芯或多模光纤的成像系统中,存在着诸多瓶颈问题限制其成像分辨率与精度。简要介绍了荧光成像、定量相位成像、散斑成像、光谱成像等多种光纤成像模态中应用深度学习解决瓶颈问题的代表性研究工作,并讨论了深度学习与光纤成像交叉研究领域的现有瓶颈,展望了智能光纤成像系统的应用前景。Fiber optic imaging technology can achieve high-resolution imaging in narrow areas due to the small size and flexibility of optical fibers.Fiber optic imaging can also be employed in biomedical research and industrial inspections.However,there are bottleneck problems in multi-core and multi-mode fiber imaging systems,limiting their resolution and accuracy.This paper briefly introduces representative research on the applications of deep learning to address these bottleneck problems in various fiber imaging modalities such as fluorescence imaging,quantitative phase imaging,speckle imaging,and multispectral imaging.Existing bottleneck in this interdisciplinary research field involving deep learning and fiber optic imaging are also discussed.Additionally,we envision the broad application prospects of intelligent fiber optic imaging systems.

关 键 词：光纤成像 深度学习 多芯光纤 多模光纤 内窥成像 

分 类 号：O439[机械工程—光学工程]