Restoration of Multilayered Single-Photon 3D LiDAR Images

This paper presents a new algorithm for the restoration of multilayered three-dimensional laser detection and ranging (3D Lidar) images. For multilayered targets such as semi-transparent surfaces or when the transmitted light of the laser beam is incident on multiple surfaces at different depths, the returned signal may contain multiple peaks. Considering the Poisson statistics of these observations leads to a convex data fidelity term that is regularized using appropriate functions accounting for the spatial correlation between pixels and the sparse depth repartition of targets. More precisely, the spatial correlation is introduced using a convex total variation (TV) regularizer, and a collaborative sparse prior is used to introduce the depth prior knowledge. The resulting minimization problem is solved using the alternating direction method of multipliers (ADMM) that offers good convergence properties. The algorithm was validated using field data representing a man standing 1 meter behind camouflage, at an approximate stand-off distance of $230$m from the system. The results show the benefit of the proposed strategy in that it improves the quality of the imaged objects at different depths and under reduced acquisition times.