Multi-scale and collaborative photon processing for 3D imaging through atmospheric obscurants using an array Gm-APD LiDAR

The limited statistical frames data and strong backscattering interference from atmospheric obscurants result in an ultra-low signal-to-background ratio (SBR) and photon per pixel (PPP) regime, which seriously limits the depth imaging capability of array Gm-APD LiDAR in strong scattering environments. Here, we propose a novel estimation algorithm, multi-scale and collaborative photon processing for 3D imaging algorithm (MCPPA), for depth imaging through high levels of atmospheric obscurant. It adopts a three-step strategy, including data preprocessing and guided image generation, signal extraction of spatio-temporal frequency collaborative photon processing, and image fusion output of multi-scale collaborative photon processing, to reduce the statistical frames data requirements. It has been successfully demonstrated in different attenuation lengths and atmospheric obscurants. Especially when the visibility is 1.7 km, we acquire depth image through dense fog equivalent to 1.5 attenuation lengths at distances of 1.4 km by using only 200 statistical frames data with PPP of 2.34 and SBR as low as 0.0091. This study has great potential for rapid depth imaging of dynamic targets under extreme weather conditions.