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Passive non-line-of-sight imaging for moving targets with an event camera

Chinese Optics Letters 2023年第6期21卷 54-60页

作者：王丛赫贺与同王霞黄泓皓闫昌达张鑫陈宏伟 Key Laboratory of Photoelectronic imaging Technology and System of Ministry of Education of China School of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China Beijing National Research Center for Information Science and Technology(BNRist) Department of Electronic EngineeringTsinghua UniversityBeijing 100084China

non-line-of-sight[NLOS]imaging is an emerging technique for detecting objects behind obstacles or around *** studies on passive NLOS mainly focus on steady-state measurement and reconstruction methods,which show limitations in recognition of moving *** the best of our knowledge,we propose a novel event-based passive NLOS imaging *** acquire asynchronous event-based data of the diffusion spot on the relay surface,which contains detailed dynamic information of the NLOS target,and efficiently ease the degradation caused by target *** addition,we demonstrate the event-based cues based on the derivation of an event-NLOS forward ***,we propose the first event-based NLOS imaging data set,EM-NLOS,and the movement feature is extracted by time-surface *** compare the reconstructions through event-based data with frame-based *** event-based method performs well on peak signal-to-noise ratio and learned perceptual image patch similarity,which is 20%and 10%better than the frame-based method.

关键词： non-line-of-sight imaging event camera event-based representation

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Physics-constrained deep-inverse point spread function model:toward non-line-of-sight imaging reconstruction

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Advanced Photonics Nexus 2024年第2期3卷 90-99页

作者： Su Wu Chan Huang Jing Lin Tao Wang Shanshan Zheng Haisheng Feng Lei Yu Chinese Academy of Sciences Anhui Institute of Optics and Fine MechanicsHefeiChina Hefei University of Technology School of PhysicsDepartment of Optical EngineeringHefeiChina Hefei Normal University Department of Chemical and Chemical EngineeringHefeiChina University of Science and Technology of China Science Island Branch of Graduate SchoolHefeiChina

non-line-of-sight(NLOS)imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse *** imaging method has garnered significant attention in diverse domains,including remote sensing,rescue operations,and intelligent driving,due to its wide-ranging potential ***,accurately modeling the incident light direction,which carries energy and is captured by the detector amidst random diffuse reflection directions,poses a considerable *** challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging,which are crucial for achieving high-quality *** this study,we propose a point spread function(PSF)model for the NLOS imaging system utilizing ray tracing with random ***,we introduce a reconstruction method,termed the physics-constrained inverse network(PCIN),which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural *** PCIN approach initializes the parameters randomly,guided by the constraints of the forward PSF model,thereby obviating the need for extensive training data sets,as required by traditional deep-learning *** alternating iteration and gradient descent algorithms,we iteratively optimize the diffuse reflection angles in the PSF model and the neural network *** results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data ***,the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.

关键词： non-line-of-sight imaging point spread function model deep learning

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