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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 10 — Apr. 1, 2010
  • pp: B9–B17

Compressive video sensors using multichannel imagers

Mohan Shankar, Nikos P. Pitsianis, and David J. Brady  »View Author Affiliations

Applied Optics, Vol. 49, Issue 10, pp. B9-B17 (2010)

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We explore the possibilities of obtaining compression in video through modified sampling strategies using multichannel imaging systems. The redundancies in video streams are exploited through compressive sampling schemes to achieve low power and low complexity video sensors. The sampling strategies as well as the associated reconstruction algorithms are discussed. These compressive sampling schemes could be implemented in the focal plane readout hardware resulting in drastic reduction in data bandwidth and computational complexity.

© 2010 Optical Society of America

OCIS Codes
(100.6640) Image processing : Superresolution
(110.1758) Imaging systems : Computational imaging

Original Manuscript: October 1, 2009
Manuscript Accepted: November 7, 2009
Published: February 3, 2010

Mohan Shankar, Nikos P. Pitsianis, and David J. Brady, "Compressive video sensors using multichannel imagers," Appl. Opt. 49, B9-B17 (2010)

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  1. Moving Picture Experts Group, “http://www.mpeg.org..”
  2. D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. K. Kelly, and R. G. Baraniuk, “A new camera architecture based on optical-domain compression,” Proc. SPIE 6065, 606509 (2006). [CrossRef]
  3. N. P. Pitsianis, D. J. Brady, and X. Sun, “Sensor-layer image compression based on the quantized cosine transform,” Proc. SPIE 5817, 250-257 (2005). [CrossRef]
  4. N. P. Pitsianis, D. J. Brady, A. Portnoy, X. Sun, T. Suleski, M. A. Fiddy, M. R. Feldman, and R. D. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006). [CrossRef]
  5. D. J. Brady, N. P. Pitsianis, X. Sun, and P. Potuluri, “Compressive sampling and signal inference,” U.S. patent 7,432,843(7 October 2008).
  6. D. J. Brady, N. P. Pitsianis, X. Sun, and P. Potuluri, “Compressive sampling and signal inference,” U.S. patent 7,463,174(9 December 2008).
  7. D. J. Brady, N. P. Pitsianis, X. Sun, and P. Potuluri, “Compressive sampling and signal inference,” U.S. patent 7,463,179(9 December 2008).
  8. J. Tanida, T. Kumagai, K. Yamada, S. Miyatake, K. Ishida, T. Marimoto, N. Kondou, D. Miyazaki, and Y. Ichioka, “Thin observation module by bound optics (TOMBO): concept and experimental verifiligcation,” Appl. Opt. 40, 1806-1813(2001). [CrossRef]
  9. L. Hong, “Superresolution video reconstruction,” Proc. SPIE 5022, 631-642 (2003).
  10. R. R. Schultz and R. L. Stevenson, “Extraction of high-resolution frames from video sequences,” IEEE Trans. Image Process. 5, 996-1011 (1996). [CrossRef] [PubMed]
  11. A. Tekalp, M. Ozkan, and M. Sezan, “High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration,” IEEE Trans. Acoust. Speech Signal Process. 3, 169-172 (1992).
  12. M. Shankar, R. Willett, N. Pitsianis, T. Schulz, R. Gibbons, R. T. Kolste, J. Carriere, C. Chen, D. Prather, and D. Brady, “Thin infrared imaging systems through multichannel sampling,” Appl. Opt. 47, B1-B10 (2008). [CrossRef] [PubMed]
  13. A. Portnoy, N. Pitsianis, X. Sun, D. Brady, R. Gibbons, A. Silver, R. Te Kolste, C. Chen, T. Dillon, and D. Prather, “Design and characterization of thin multiple aperture infrared cameras,” Appl. Opt. 48, 2115-2126 (2009). [CrossRef] [PubMed]
  14. A. D. Portnoy, N. P. Pitsianis, X. Sun, and D. J. Brady, “Multichannel sampling schemes for optical imaging systems,” Appl. Opt. 47, B76-B85 (2008). [CrossRef] [PubMed]
  15. M. Shankar, N. P. Pitsianis, and D. J. Brady, “Spatio-temporal sampling for video,” Proc. SPIE 7076, 707604 (2008). [CrossRef]
  16. W. H. Richardson, “Bayesian-based iterative method of image restoration,” J. Opt. Soc. Am. 62, 55-59 (1972). [CrossRef]
  17. L. B. Lucy, “An iterative technique for the rectifiligcation of observed distributions,” Astron. J. 79, 745-754 (1974). [CrossRef]

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