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Photon-Efficient 3D Imaging with A Non-local Neural Network

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12351)

Abstract

Photon-efficient imaging has enabled a number of applications relying on single-photon sensors that can capture a 3D image with as few as one photon per pixel. In practice, however, measurements of low photon counts are often mixed with heavy background noise, which poses a great challenge for existing computational reconstruction algorithms. In this paper, we first analyze the long-range correlations in both spatial and temporal dimensions of the measurements. Then we propose a non-local neural network for depth reconstruction by exploiting the long-range correlations. The proposed network achieves decent reconstruction fidelity even under photon counts (and signal-to-background ratio, SBR) as low as 1 photon/pixel (and 0.01 SBR), which significantly surpasses the state-of-the-art. Moreover, our non-local network trained on simulated data can be well generalized to different real-world imaging systems, which could extend the application scope of photon-efficient imaging in challenging scenarios with a strict limit on optical flux. Code is available at https://github.com/JiayongO-O/PENonLocal.

Keywords

Photon-efficient imaging Long-range correlation Non-local network Depth reconstruction 

Notes

Acknowledgements

We acknowledge funding from National Key R&D Program of China under Grants 2017YFA0700800 and 2018YFB0504300, National Natural Science Foundation of China under Grants 61671419 and 61771443, the Shanghai Municipal Science and Technology Major Project (2019SHZDZX01), the Shanghai Science and Technology Development Funds (18JC1414700), and the Fundamental Research Funds for the Central Universities (WK2340000083).

Supplementary material

504443_1_En_14_MOESM1_ESM.pdf (482 kb)
Supplementary material 1 (pdf 481 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Science and Technology of ChinaHefeiChina

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