Joint Reconstruction of Multitemporal or Multispectral Single-Photon 3D LiDAR Images

The aim of this paper is to propose a specialized algorithm to process Multitemporal or Multispectral 3D single-photon Lidar images. Of particular interest are challenging scenarios often encountered in real world, i.e., imaging through obscurants such as water, fog or imaging multilayered targets such as target behind camouflage. To restore the data, the algorithm accounts for data Poisson statistics and available prior knowledge regarding target depth and reflectivity estimates. More precisely, it accounts for (a) the non-local spatial correlations between pixels, (b) the spatial clustering of target returned photons and (c) spectral and temporal correlations between frames. An alternating direction method of multipliers (ADMM) algorithm is used to minimize the resulting cost function since it offers good convergence properties. The algorithm is validated on real data which show the benefit of the proposed strategy especially when dealing with multi-dimensional 3D data.