Noise-tolerant depth image estimation for array Gm-APD LiDAR through atmospheric obscurants

The limited statistical frame data and substantial backscattering interference from atmospheric obscurants result in a photon-starved regime, which seriously limits the depth imaging capability of array Gm-APD LiDAR in strong scattering environments. Here, we propose a depth image estimation algorithm through atmospheric obscurants that can significantly improve target integrity when signal photons are scarce. At the signal level, based on the established array Gm-APD LiDAR smoke echo model, this algorithm enhances the number of signal photons by constructing multi-scale superpixels. At the image level, efficient noise removal and improvement of target integrity are achieved by using the spatial similarity features and the edge information of reconstructed images at different scales. It has been successfully demonstrated in different attenuation lengths and atmospheric obscurants. Especially when the visibility is 1.7 km, we acquire depth images through dense fog equivalent to 1.5 attenuation lengths at distances of 1.4 km by using only 800 statistical frames data. This study has great potential for rapid depth imaging of dynamic targets under extreme weather conditions.