TR2010-020

Coded Strobing Photography: Compressive Sensing of High-Speed Periodic Events


    •  Veeraraghavan, A., Reddy, D., Raskar, R., "Coded Strobing Photography: Compressive Sensing of High-speed Periodic Events", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 4, pp. 671-686, April 2010.
      BibTeX TR2010-020 PDF
      • @article{Veeraraghavan2010apr,
      • author = {Veeraraghavan, A. and Reddy, D. and Raskar, R.},
      • title = {Coded Strobing Photography: Compressive Sensing of High-speed Periodic Events},
      • journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      • year = 2010,
      • volume = 33,
      • number = 4,
      • pages = {671--686},
      • month = apr,
      • issn = {0163-8828},
      • url = {https://www.merl.com/publications/TR2010-020}
      • }
  • Research Area:

    Computer Vision

Abstract:

We show that, via temporal modulation, one can observe a high-speed periodic event well beyond the abilities of a low-frame camera. By strobing the exposure with unique sequences within the integration time of each frame, we take coded projections of dynamic events. From a sequence of such frames, we reconstruct a high-speed video of the high frequency periodic process. Strobing is used in entertainment, medical imaging and industrial inspection to generate lower beat frequencies. But this is limited to scenes with a detectable single dominant frequency and requires high-intensity lighting. In this paper, we address the problem of sub-Nyquist sampling of periodic signals and show designs to capture and reconstruct such signals. The key result is that for such signals the Nyquist rate constraint can be imposed on strobe-rate rather than the sensor-rate. The technique is based on intentional aliasing of the frequency components of the periodic signal while the reconstruction algorithm exploits recent advances in sparse representations and compressive sensing. We exploit the sparsity of periodic signals in Fourier domain to develop reconstruction algorithms that are inspired by compressive sensing.

 

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