基于三维卷积的Gm-APD单光子激光雷达时空联合去噪算法OA

Geiger-mode Avalanche Photodiode(Gm-APD)imaging is typically accompanied by sig-nificant noise interference,which poses challenges to the accurate extraction of target information.To address the issues of low signal-to-noise ratio(SNR)and extremely weak echo signals,this pa-per proposes a spatiotemporal joint denoising method based on 3 D convolution.The algorithm le-verages the high correlation of Gm-APD LiDAR echo signals in both the temporal and spatial do-mains,utilizing a designed 3 D convolution kernel to fully extract and integrate temporal and spatial information.The method first counts the histogram of the trigger frequency of each pixel across multiple consecutive frames in the temporal domain.Then,it employs the designed 3 D con-volution kernel to extract temporal and spatial information,obtaining an optimized trigger histo-gram,and distinguishing target points from noise points through threshold segmentation.Experi-ments are conducted on actual Gm-APD LiDAR datasets.The experimental results demonstrate that,compared to the peak-threshold method,the proposed method achieves a 39.96％improve-ment in target recovery and a 9.15-fold increase in SNR under the condition of 200-frame fusion denoising,indicating enhanced denoising performance.