Denoising and Reconstruction Algorithm for Gm-APD Imaging Lidar based on Morphological Filtering

When using Geiger mode Avalanche Photo Diode (Gm-APD) array lidar for long-distance imaging, the few echo photons make it challenging to get the target position. To solve these problems, this paper proposes a spatial correlation extraction algorithm combined with morphological filtering (SCMF), which uses the spatial correlation of the target to superposition the weight of the pixel histogram, increasing the number of statistical frames, improving the signal-to-noise (SNR) of pixel statistical data and accurately extracting the distance value of the target pixel. Spatial correlation also improves the real-time imaging of the system. According to the time-domain spatial dispersion characteristics of residual noise pixels of small intensity threshold, a local spatial distance correlation logic method is proposed, which only preserves the pixel groups with similar spatial distances and removes the stray background noise pixels. Because the number of pixels in the target pixel group is more than the noise group, a spatial filter module is constructed using morphological filtering to remove the remaining blocky noise group and preserve the target pixel group. The proposed method can achieve long-distance imaging in 0.02s acquisition time through outdoor real imaging experiments. Under the echo condition of 0.0152 Signal to Background ratio (SBR), the SCMF method has 76% target restoration, and the reconstructed image SNR can improve 23 times compared with the peak-picking method, a great improvement has been made in the reconstruction of image denoising.