This paper proposes a new algorithm to restore 3D single-photon Lidar images obtained under challenging realistic scenarios which include imaging multilayered targets such as semi-transparent surfaces or imaging through obscurants such as scattering media (e.g., water, fog). The Data restoration and exploitation is achieved by minimising an appropriate cost-function accounting for the data Poisson statistics and the available prior knowledge regarding the depth and reflectivity estimates. The proposed algorithm takes into account (i) the non-local spatial correlations between pixels, by using a convex non-local total variation (TV) regularizer, and (ii) the clustered nature of the returned photons, by using a collaborative sparse prior. The resulting minimization problem is solved using the alternating direction method of multipliers (ADMM) that offers good convergence properties. The algorithm is validated using both synthetic and real data which show the benefit of the proposed strategy in the sparse regime due to a fast acquisition or in presence of a high background due to obscurants.
|Title of host publication||2018 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)|
|Number of pages||5|
|Publication status||Published - 30 Aug 2018|
|Name||Sensor Array and Multichannel Signal Processing Workshop (SAM)|