基于时空域信息提取的多模光纤成像  

Multimode Fiber Imaging Based on Temporal-Spatial Information Extraction

作　　者：朱润泽 徐飞[1] Zhu Runze;Xu Fei(College of Engineering and Applied Sciences,Nanjing University,Nanjing 210093,Jiangsu,China)

机构地区：[1]南京大学现代工程与应用科学学院,江苏南京210093

出　　处：《激光与光电子学进展》2023年第11期127-147,共21页Laser & Optoelectronics Progress

基　　金：江苏省重点研发计划(BE2020113)。

摘　　要：多模光纤内部的模式特性使其包含丰富的时空域信息。基于时空域信息提取的多模光纤成像作为一种新兴的光纤成像方法,具有器件尺寸小、分辨率高、信息容量大、侵入损伤小等优势,具备成为新一代高分辨率、低损伤内窥镜的潜力。本文总结了多模光纤成像的基本方法和相关进展,并介绍了机器学习与多模光纤成像结合的相关工作。此外,面向多模光纤成像的实际应用,讨论了动态干扰下多模光纤成像的方法和相关进展以及多模光纤成像在成像性能和质量上的不足。最后对多模光纤成像进行总结并加以展望。Multimode fiber(MMF)contains rich temporal and spatial information because of the mode characteristics.As a novel optical fiber imaging method,MMF imaging based on temporal-spatial information extraction has the advantages of small device size,high resolution,large information capacity,and minimal invasion and has the potential to become a new generation of high-resolution and low-invasive endoscope.This review summarizes the basic methods and related progress of MMF imaging and introduces the related work of combining machine learning with MMF imaging.In addition,for the practical application of MMF imaging,we discuss the methods and related progress of MMF imaging under dynamic perturbation and the limitation of MMF imaging performance and quality.Finally,MMF imaging is summarized and prospected.

关 键 词：光纤成像 多模光纤 时空域信息 散斑成像 内窥成像 机器学习 

分 类 号：O439[机械工程—光学工程]