An Adaptive Photon Count Reconstruction Algorithm for Sparse Count and Strong Noise Count Data With Low Signal Background Ratio

Abstract

Single-photon lidar detection data in applications can show different characteristics: sparse count data and strong noise count data with low signal-to-background ratio (SBR), making it difficult to accurately reconstruct depth and intensity information. The existing statistical-based algorithms can achieve reconstruction, but they may lack compatibility for sparse counting and strong noise counting cases which will switch to each other in practical applications. In this paper, an adaptive photon count reconstruction algorithm for sparse count and strong noise count data with low SBR is proposed based on the difference in temporal distribution characteristics between the echo and noise count data. The aggregation characteristic of echo count data in time dimension is proposed to adaptively separate the echo and noise regions in the histogram to reduce the noise interference, and based on the relative difference between count levels in the time neighborhood, an objective function is constructed to reconstruct depth and intensity using optimization. The reconstruction results based on simulated and experimental data confirm that the reconstruction accuracies under both sparse counting and strong noise counting cases are effectively improved under low SBR conditions. Compared with the state-of-the-art algorithms, the depth absolute error is reduced by nearly 50%, the edge error is reduced by an order of magnitude and the proportion of correctly reconstructed pixels reaches 90% when SBR = 0.1. It shows the potential of the proposed algorithm for improving target recognition ability and all-day imaging.