Photon-efficient imaging with the single-photon light detection and ranging captures the three-dimensional structure of a scene by only a few detected signal photons per pixel. However, the existing computational methods for photon-efficient imaging are pretuned on a restricted scenario or trained on simulated datasets. When applied to realistic scenarios whose signal-to-background ratios and other hardware-specific properties differ from those of the original task, the model performance often significantly deteriorates. In this paper, we present a domain adversarial adaptation design to alleviate this domain shift problem by exploiting unlabeled real-world data, with significant resource savings. This method demonstrates superior performance on simulated and real-world experiments using our home-built up-conversion single-photon imaging system, which provides an efficient approach to bypass the lack of ground-truth depth information in implementing computational imaging algorithms for realistic applications.

• Received 14 May 2022
• Revised 1 August 2022
• Accepted 11 October 2022

DOI:https://doi.org/10.1103/PhysRevApplied.18.054048