Computational Single-Photon Depth Imaging Without Transverse Regularization

Depth profile reconstruction of a scene at low light levels using an active imaging setup has wide-ranging applications in remote sensing. In such low-light imaging scenarios, single-photon detectors are employed to time-resolve individual photon detections. However, even with single-photon detectors, current frameworks are limited to using hundreds of photon detections at each pixel to mitigate Poisson noise inherent in light detection. In this paper, we discuss two pixelwise imaging frameworks that allow accurate reconstruction of depth profiles using small numbers of photon detections. The first framework addresses the problem of depth reconstruction of an opaque target, in which it is assumed that each pixel contains exactly one reflector. The second framework addresses the problem of reconstructing multiple-depth pixels. In each scenario, our framework achieves photon efficiency by combining accurate statistics for individual photon detections with a longitudinal sparsity constraint tailored to the imaging problem. We demonstrate the photon efficiencies of our frameworks by comparing them with conventional imagers that use more naive models based on high light-level assumptions.