摘要
非视域(Non-Line-of-Sight,NLoS)成像是近年来发展起来的一项新兴技术,其通过分析成像场景中的中介面信息来重建隐藏场景,实现了“拐弯成像”的效果,在多个领域有巨大的应用价值。本文主要针对NLoS成像重建算法进行综述性研究。考虑到目前NLoS成像分类存在交叉和非独立现象,本文基于物理成像模式和算法模型的不同特点,对其进行了独立的重新分类。根据提出的分类标准分别对传统和基于深度学习的NLoS成像重建算法进行了归纳总结,对代表性算法的发展现状进行了概述,推导了典型方法的实现原理,并对比了传统重建方法和基于深度学习的NLoS成像重建算法的重建应用结果。总结了NLoS成像目前存在的挑战和未来的发展方向。该研究对不同类型的NLoS成像进行了较为全面的梳理,对NLoS成像重建算法在内的一系列研究的进一步发展有着一定的支撑和推动作用。
Non-line-of-sight(NLoS)imaging is a promising technique developed in recent years,which can reconstruct hidden scenes by analyzing the information in the intermediate surface,and"see around the corner",and has strong application value in many fields.In this paper,we review the reconstruction algorithm for NLoS imaging tasks.Firstly,considering the crossover and non-independent phenomena existing in the NLoS imaging classification,we use the different features of physical imaging models and algorithm models to reclassify them.Secondly,according to the proposed classification criteria,we respectively review the traditional and deep learning-based NLoS imaging reconstruction algorithms,summarize the state-of-the-art algorithms,and derive the implement principle.We also compare the results of deep learning-based and traditional NLoS imaging reconstruction algorithms for reconstruction tasks.Finally,the current challenges and the future development of NLoS imaging are summarized.Different types of NLoS imaging reconstruction algorithms are comprehensively analyzed in this review,which provides important support for the further development of NLoS imaging reconstruction algorithms.
作者
赵禄达
董骁
徐世龙
胡以华
张鑫源
钟易成
ZHAO Lu-da;DONG Xiao;XU Shi-long;HU Yi-hua;ZHANG Xin-yuan;ZHONG Yi-cheng(College of Electronic Engineering,National University of Defence Technology,Hefei 230037,China;State Key Laboratory of Pulsed Power Laser Technology,National University of Defense Technology,Hefei 230037,China;Anhui Province Key Laboratory of Electronic Restriction,National University of Defense Technology,Hefei 230037,China;Unit 77126 of the PLA,Kaiyuan 661600,China)
出处
《中国光学（中英文）》
EI
CAS
CSCD
北大核心
2023年第3期479-499,共21页
Chinese Optics
基金
国家自然科学基金(No.61871389,No.62201597)
国防科技大学科研计划重大项目(No.ZK18-01-02,No.ZK22-35)
国防科技大学自主科研创新基金(No.22-ZZCX-07)
国防科技创新特区项目(No.22-TQ23-07-ZD-01-001)
军事类研究生资助课题(No.JY2021B042)
湖南省研究生科研创新项目(No.CX20220045)。
关键词
非视域成像
重建算法
成像模式
深度学习
non-line-of-sight imaging
reconstruction algorithm
imaging mode
deep learning
