2022/01/07 by Yiwei Chen, Chen, Yiwei, Gongxin Yao +9 · 1 citation
Biochemistry, Genetics and Molecular Biology · Engineering · Physics and Astronomy · #Advanced Fluorescence Microscopy Techniques #Advanced Optical Sensing Technologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Photoacoustic and Ultrasonic Imaging #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2201.02475
openalex publication_date 2022/01/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Photon-efficient imaging with the single-photon light detection and ranging (LiDAR) captures the three-dimensional (3D) structure of a scene by only a few detected signal photons per pixel. However, the existing computational methods for photon-efficient imaging are pre-tuned on a restricted scenario or trained on simulated datasets. When applied to realistic scenarios whose signal-to-background ratios (SBR) 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.