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)
孙佳伟 1陈照青 1赵斌 2李学龙3
作者信息
- 1. 上海人工智能实验室智能光电中心,上海 200232
- 2. 上海人工智能实验室智能光电中心,上海 200232;西北工业大学光电与智能研究院,陕西 西安 710072
- 3. 上海人工智能实验室智能光电中心,上海 200232;中国电信人工智能研究院,北京 100033
- 折叠
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.
Abstract
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Key words
fiber optic imaging/deep learning/multi-core fiber/multi-mode fiber/endoscopy引用本文复制引用
出版年
2024
NETL
NSTL
万方数据