  Capturing depth and reflectivity images at low light levels from active
illumination of a scene has wide-ranging applications. Conventionally, even
with single-photon detectors, hundreds of photon detections are needed at each
pixel to mitigate Poisson noise. We develop a robust method for estimating
depth and reflectivity using on the order of 1 detected photon per pixel
averaged over the scene. Our computational imager combines physically accurate
single-photon counting statistics with exploitation of the spatial correlations
present in real-world reflectivity and 3D structure. Experiments conducted in
the presence of strong background light demonstrate that our computational
imager is able to accurately recover scene depth and reflectivity, while
traditional maximum-likelihood based imaging methods lead to estimates that are
highly noisy. Our framework increases photon efficiency 100-fold over
traditional processing and also improves, somewhat, upon first-photon imaging
under a total acquisition time constraint in raster-scanned operation. Thus our
new imager will be useful for rapid, low-power, and noise-tolerant active
optical imaging, and its fixed dwell time will facilitate parallelization
through use of a detector array.
