Improved algorithm of non-line-of-sight imaging based on the Bayesian statistics

Abstract

The recovery of obscured objects is an important goal in imaging that has been approached by exploiting coherence properties, ballistic photons, and penetrating wavelengths. In this paper, a robust reconstruction non-line-of-sight (NLOS) algorithm was proposed based on the Bayesian statistics, using the temporal, spatial, and intensity information of each signal. Compared with conventional back-projection methods, this algorithm is able to handle random errors in data and to image occluded objects with a higher quality. An adjustable compensation mechanism in our method is effective in dealing with the diversity of objects with regard to reflective characteristics. Our algorithm was demonstrated to be efficient with both simulated and experimental data. In addition, we discuss the advantages over existing methods and further improvement of the proposed algorithm.

© 2019 Optical Society of America

Compressed sensing for active non-line-of-sight imaging

Jun-Tian Ye, Xin Huang, Zheng-Ping Li, and Feihu Xu
Opt. Express 29(2) 1749-1763 (2021)

Edge re-projection method for high-quality edge reconstruction in non-line-of-sight imaging

Zhengyang Shi, Xia Wang, Yang Li, Zhibin Sun, and Wenxi Zhang
Appl. Opt. 59(6) 1793-1800 (2020)

Fast back-projection for non-line of sight reconstruction

Victor Arellano, Diego Gutierrez, and Adrian Jarabo
Opt. Express 25(10) 11574-11583 (2017)

A calibration scheme for non-line-of-sight imaging setups

Jonathan Klein, Martin Laurenzis, Matthias B. Hullin, and Julian Iseringhausen
Opt. Express 28(19) 28324-28342 (2020)

Non-line-of-sight-imaging using dynamic relay surfaces

Marco La Manna, Ji-Hyun Nam, Syed Azer Reza, and Andreas Velten
Opt. Express 28(4) 5331-5339 (2020)