4 accepted papers
Time-correlated single-photon technology is emerging as an important approach to 3D Imaging. This paper presents a reconstruction algorithm that exploits data statistics and multi-scale information to deliver clean depth and reflectivity images together with associated uncertainty maps. The statisti…
This paper presents a new Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data acquired in extreme conditions. We focus on imaging through obscurants (i.e., fog, water) leading to high and possibly non-uniform background noise. The proposed hierarchical Bayesian…
This paper presents a novel nonlinear hyperspectral mixture model and its associated supervised unmixing algorithm. The model assumes a linear mixing model corrupted by an additive term which accounts for multiple scattering nonlinearities (NL). The proposed model generalizes bilinear models by taki…
This paper presents a new Bayesian model and associated algorithm for depth and intensity profiling using full waveforms from time-correlated single-photon counting (TCSPC) measurements when the photon count in very low. The model represents each Lidar waveform as an unknown constant background leve…