深 度 学 习 在 光 纤 成 像 中 的 应 用 进 展(特 邀)

Translated title of the contribution: Advances in Deep Learning Based Fiber Optic Imaging (Invited)

Sun Jiawei, Chen Zhaoqing, Zhao Bin, Li Xuelong

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

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.

Translated title of the contributionAdvances in Deep Learning Based Fiber Optic Imaging (Invited)
Original languageChinese (Traditional)
Article number1611004
JournalLaser and Optoelectronics Progress
Volume61
Issue number16
DOIs
StatePublished - Aug 2024

Fingerprint

Dive into the research topics of 'Advances in Deep Learning Based Fiber Optic Imaging (Invited)'. Together they form a unique fingerprint.

Cite this