A photon-number-based systematic algorithm for range image recovery of GM-APD lidar under few-frames detection

The GM-APD lidar can detect the single-photon level echo signal and obtain three-dimensional range informa-tion, but the imaging results are greatly affected by noise and statistical frame number. Considering these problems, this paper proposes a photon-number systematic (PNS) algorithm under a few statistical frames in low SNR (signal-to-noise ratio). It includes three parts: (a) We realize range image recovery based on the signal photon number histogram with a match filtering algorithm by using the proposed normalized discrete waveform functions. (b) We propose a two-dimensional double-threshold approximation denoising algorithm, which is used for image-level denoising. (c) The neighborhood threshold smoothing algorithm is proposed to supplement the target missing pixels. It is verified by Monte Carlo simulation and experimental results that the proposed algo-rithm has a high recovery ratio, denoising, and fidelity performance under few-frames detection in different SNRs conditions. The experimental largest target recovery ratio of the proposed algorithm is about 92%, the peak signal-to-noise ratio exceeds 16.4, and the maximum average measure of structural similarity is more than 85% under 500 statistical frames. This paper provides a new processing scheme for GM-APD lidar range image re-covery under few-frames detection and low SNR, it lays the foundation for real-time and all-day monitoring.