Robust and Guided Super-resolution for Single-Photon Depth Imaging via a Deep Network

Alice Ruget, Stephen McLaughlin, Robert K. Henderson, Istvan Gyongy, Abderrahim Halimi, Jonathan Leach*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The number of applications that use depth imaging is rapidly increasing, e.g. 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 to take advantage of the multiple features that can be extracted from a camera's histogram data. 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.

Original languageEnglish
Title of host publication2021 29th European Signal Processing Conference (EUSIPCO)
Subtitle of host publicationProceedings
PublisherIEEE Xplore
Number of pages5
ISBN (Electronic)9789082797060
Publication statusPublished - 8 Dec 2021
Event29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference29th European Signal Processing Conference, EUSIPCO 2021

Keywords / Materials (for Non-textual outputs)

  • Deep network
  • Guided super-resolution
  • LiDAR waveform
  • Robust reconstruction
  • Unet


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