2021/03/18 by Abderrahim Halimi, Halimi, Abderrahim, Aurora Maccarone +7 · 1 citation
Engineering · Environmental Science · Physics and Astronomy · #Advanced Optical Sensing Technologies #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Photoacoustic and Ultrasonic Imaging #Remote Sensing and LiDAR Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2103.10122
openalex publication_date 2021/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
3D Lidar imaging can be a challenging modality when using multiple\nwavelengths, or when imaging in high noise environments (e.g., imaging through\nobscurants). This paper presents a hierarchical Bayesian algorithm for the\nrobust reconstruction of multispectral single-photon Lidar data in such\nenvironments. The algorithm exploits multi-scale information to provide robust\ndepth and reflectivity estimates together with their uncertainties to help with\ndecision making. The proposed weight-based strategy allows the use of available\nguide information that can be obtained by using state-of-the-art learning based\nalgorithms. The proposed Bayesian model and its estimation algorithm are\nvalidated on both synthetic and real images showing competitive results\nregarding the quality of the inferences and the computational complexity when\ncompared to the state-of-the-art algorithms.\n