High spatial resolution time-of-flight imaging

Fengqiang Li*, Huaijin Chen, Chia Kai Yeh, Ashok Veeraraghavan, Oliver Strides Cossairt

*Corresponding author for this work

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

2 Scopus citations


Continuous wave time-of-flight (ToF) cameras have been rapidly gaining widespread adoption in many applications due to their cost effectiveness, simplicity, and compact size. However, the current generation of ToF cameras suffers from low spatial resolution due to physical fabrication limitations. In this paper, we propose an imaging architecture to achieve high spatial resolution ToF imaging using optical multiplexing and compressive sensing (CS). Our approach is based on the observation that, while depth is non-linearly related to ToF pixel measurements, a phasor representation of captured images results in a linear image formation model. We utilize this property to develop a CS-based technique that is used to recover high resolution 3D images. Based on the proposed architecture, we developed a prototype 1-megapixel compressive ToF camera that achieves as much as 4 x improvement in spatial resolution. We believe that our proposed architecture provides a simple and low-cost solution to improve the spatial resolution of ToF and related sensors.

Original languageEnglish (US)
Title of host publicationComputational Imaging III
EditorsAmit Ashok, Jonathan C. Petruccelli, Abhijit Mahalanobis, Lei Tian
ISBN (Electronic)9781510618497
StatePublished - Jan 1 2018
EventComputational Imaging III 2018 - Orlando, United States
Duration: Apr 15 2018Apr 17 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherComputational Imaging III 2018
Country/TerritoryUnited States


  • Compressing Sensing
  • Inverse Problem
  • Superresolution
  • Time-of-Flight Imaging

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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