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Temporal Compressive Sensing for Video

  • Patrick LlullEmail author
  • Xin Yuan
  • Xuejun Liao
  • Jianbo Yang
  • David Kittle
  • Lawrence Carin
  • Guillermo Sapiro
  • David J. Brady
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)

Abstract

Video camera architects must design cameras capable of high-quality, dynamic event capture, while adhering to power and communications constraints. Though modern imagers are capable of both simultaneous spatial and temporal resolutions at micrometer and microsecond scales, the power required to sample at these rates is undesirable. The field of compressive sensing (CS) has recently suggested a solution to this design challenge. By exploiting physical-layer compression strategies, one may overlay the original scene with a coding sequence to sample at sub-Nyquist rates with virtually no additional power requirement. The underlying scene may be later estimated without significant loss of fidelity. In this chapter, we cover a variety of such strategies taken to improve an imager’s temporal resolution. Highlighting a new low-power acquisition paradigm, we show how a video sequence of high temporal resolution may be reconstructed from a single video frame taken with a low-framerate camera.

Keywords

Gaussian Mixture Model Compressive Sense Transmission Function International Standard Organization Spatial Light Modulator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The research, results, and theory presented here were supported by the Knowledge Enhanced Compressive Measurement Program at the Defense Advanced Research Projects Agency, grant N660011114002. Additional support from the ONR, NGA, ARO, and NSF is acknowledged.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Patrick Llull
    • 1
    Email author
  • Xin Yuan
    • 1
  • Xuejun Liao
    • 1
  • Jianbo Yang
    • 1
  • David Kittle
    • 1
  • Lawrence Carin
    • 1
  • Guillermo Sapiro
    • 1
  • David J. Brady
    • 1
  1. 1.Department of Electrical and Computer EngineeringDuke UniversityDurhamUSA

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