Abstract
The number of applications that use depth imaging is increasing rapidly, e.g. selfdriving 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 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 down-sampled
histograms to guide the up-sampling 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. Additionally, we show that the network can be applied to other data types of SPAD
data, demonstrating the generality of the algorithm.
Original language | English |
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Pages (from-to) | 11917-11937 |
Number of pages | 21 |
Journal | Optics Express |
Volume | 29 |
Issue number | 8 |
Early online date | 1 Apr 2021 |
DOIs | |
Publication status | Published - 12 Apr 2021 |
Keywords
- eess.IV
- cs.CV
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics