  Three-dimensional imaging plays an important role in imaging applications
where it is necessary to record depth. The number of applications that use
depth imaging is increasing rapidly, and examples include self-driving
autonomous vehicles and auto-focus assist on smartphone cameras. Light
detection and ranging (LIDAR) via single-photon sensitive detector (SPAD)
arrays is an emerging technology that enables the acquisition of depth images
at high frame rates. However, the spatial resolution of this technology is
typically low in comparison to the intensity images recorded by conventional
cameras. To increase the native resolution of depth images from a SPAD camera,
we develop a deep network built specifically to take advantage of the multiple
features that can be extracted from a camera's histogram data. The network is
designed for a SPAD camera operating in a dual-mode such that it captures
alternate low resolution depth and high resolution intensity images at high
frame rates, thus the system does not require any additional sensor to provide
intensity images. The network then uses the intensity images and multiple
features extracted from downsampled histograms to guide the upsampling of the
depth. Our network provides significant image resolution enhancement and image
denoising across a wide range of signal-to-noise ratios and photon levels. We
apply the network to a range of 3D data, demonstrating denoising and a
four-fold resolution enhancement of depth.
