A boundary migration model for imaging within volumetric scattering media

Dongyu Du, Xin Jin (), Rujia Deng, Jinshi Kang, Hongkun Cao, Yihui Fan, Zhiheng Li (), Haoqian Wang, Xiangyang Ji and Jingyan Song
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Dongyu Du: Tsinghua University
Xin Jin: Tsinghua University
Rujia Deng: Tsinghua University
Jinshi Kang: Tsinghua University
Hongkun Cao: Tsinghua University
Yihui Fan: Tsinghua University
Zhiheng Li: Tsinghua University
Haoqian Wang: Tsinghua University
Xiangyang Ji: Tsinghua University
Jingyan Song: Tsinghua Innovation Center in Zhuhai

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-30948-7

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