Analysis and Improvement of Rank-Ordered Mean Algorithm in Single-Photon LiDAR
This work addresses a domain-specific problem for single-photon LiDAR systems by providing a theoretical analysis and incremental improvement to an existing noise rejection method.
The paper tackled the problem of depth estimation in single-photon LiDAR being error-prone due to background noise by analyzing the rank-ordered mean (ROM) filter and proposing an improved technique based on tight timestamp clusters. The result showed that the proposed algorithm improves depth estimation performance over ROM by 3 orders of magnitude at the same signal intensities and achieves high image fidelity at noise levels up to 17 times that of signal.
Depth estimation using a single-photon LiDAR is often solved by a matched filter. It is, however, error-prone in the presence of background noise. A commonly used technique to reject background noise is the rank-ordered mean (ROM) filter previously reported by Shin \textit{et al.} (2015). ROM rejects noisy photon arrival timestamps by selecting only a small range of them around the median statistics within its local neighborhood. Despite the promising performance of ROM, its theoretical performance limit is unknown. In this paper, we theoretically characterize the ROM performance by showing that ROM fails when the reflectivity drops below a threshold predetermined by the depth and signal-to-background ratio, and its accuracy undergoes a phase transition at the cutoff. Based on our theory, we propose an improved signal extraction technique by selecting tight timestamp clusters. Experimental results show that the proposed algorithm improves depth estimation performance over ROM by 3 orders of magnitude at the same signal intensities, and achieves high image fidelity at noise levels as high as 17 times that of signal.