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.
CITATION STYLE
Peng, J., Xiong, Z., Huang, X., Li, Z. P., Liu, D., & Xu, F. (2020). Photon-Efficient 3D Imaging with A Non-local Neural Network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12351 LNCS, pp. 225–241). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58539-6_14
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