Abstract

Effectively imaging within volumetric scattering media is of great importance and challenging especially in macroscopic applications. Recent works have demonstrated the ability to image through scattering media or within the weak volumetric scattering media using spatial distribution or temporal characteristics of the scattered field. Here, we focus on imaging Lambertian objects embedded in highly scattering media, where signal photons are dramatically attenuated during propagation and highly coupled with background photons. We address these challenges by providing a time-to-space boundary migration model (BMM) of the scattered field to convert the scattered measurements in spectral form to the scene information in the temporal domain using all of the optical signals. The experiments are conducted under two typical scattering scenarios: 2D and 3D Lambertian objects embedded in the polyethylene foam and the fog, which demonstrate the effectiveness of the proposed algorithm. It outperforms related works including time gating in terms of reconstruction precision and scattering strength. Even though the proportion of signal photons is only 0.75%, Lambertian objects located at more than 25 transport mean free paths (TMFPs), corresponding to the round-trip scattering length of more than 50 TMFPs, can be reconstructed. Also, the proposed method provides low reconstruction complexity and millisecond-scale runtime, which significantly benefits its application.

Imaging in scattering media is challenging due to signal attenuation and strong coupling of scattered and signal photons. The authors present a boundary migration model of the scattered field, converting scattered measurements in spectral form to scene information in temporal domain, and image Lambertian objects in highly scattering media.

Details

Title
A boundary migration model for imaging within volumetric scattering media
Author
Du, Dongyu 1   VIAFID ORCID Logo  ; Jin, Xin 1   VIAFID ORCID Logo  ; Deng, Rujia 1   VIAFID ORCID Logo  ; Kang, Jinshi 1   VIAFID ORCID Logo  ; Cao, Hongkun 1 ; Fan, Yihui 1 ; Li, Zhiheng 1   VIAFID ORCID Logo  ; Wang, Haoqian 1   VIAFID ORCID Logo  ; Ji, Xiangyang 2   VIAFID ORCID Logo  ; Song, Jingyan 3 

 Tsinghua University, Shenzhen International Graduate School, Shenzhen, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) 
 Tsinghua University, Department of Automation, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178); Tsinghua University, Institute for Brain and Cognitive Sciences, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178); Tsinghua University, Beijing National Research Center for Information Science and Technology, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) 
 Tsinghua Innovation Center in Zhuhai, Zhuhai, China (GRID:grid.12527.33) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2674582524
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.