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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 4 — Feb. 1, 2012
  • pp: A67–A79

Space–time compressive imaging

Vicha Treeaporn, Amit Ashok, and Mark A. Neifeld  »View Author Affiliations


Applied Optics, Vol. 51, Issue 4, pp. A67-A79 (2012)
http://dx.doi.org/10.1364/AO.51.000A67


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Abstract

Compressive imaging systems typically exploit the spatial correlation of the scene to facilitate a lower dimensional measurement relative to a conventional imaging system. In natural time-varying scenes there is a high degree of temporal correlation that may also be exploited to further reduce the number of measurements. In this work we analyze space–time compressive imaging using Karhunen–Loève (KL) projections for the read-noise-limited measurement case. Based on a comprehensive simulation study, we show that a KL-based space–time compressive imager offers higher compression relative to space-only compressive imaging. For a relative noise strength of 10% and reconstruction error of 10%, we find that space–time compressive imaging with 8×8×16 spatiotemporal blocks yields about 292× compression compared to a conventional imager, while space-only compressive imaging provides only 32× compression. Additionally, under high read-noise conditions, a space–time compressive imaging system yields lower reconstruction error than a conventional imaging system due to the multiplexing advantage. We also discuss three electro-optic space-time compressive imaging architecture classes, including charge-domain processing by a smart focal plane array (FPA). Space–time compressive imaging using a smart FPA provides an alternative method to capture the nonredundant portions of time-varying scenes.

© 2012 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.3010) Image processing : Image reconstruction techniques
(110.0110) Imaging systems : Imaging systems
(110.6980) Imaging systems : Transforms
(110.1758) Imaging systems : Computational imaging

History
Original Manuscript: October 12, 2011
Manuscript Accepted: December 16, 2011
Published: January 31, 2012

Citation
Vicha Treeaporn, Amit Ashok, and Mark A. Neifeld, "Space–time compressive imaging," Appl. Opt. 51, A67-A79 (2012)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-51-4-A67


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References

  1. W. T. Cathey and E. R. Dowski, “New paradigm for imaging systems,” Appl. Opt. 41, 6080–6092 (2002). [CrossRef]
  2. C. J. Oliver, “Optical image processing by multiplex coding,” Appl. Opt. 15, 93–106 (1976). [CrossRef]
  3. D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE, 6065, 606509 (2006). [CrossRef]
  4. M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008). [CrossRef]
  5. E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006). [CrossRef]
  6. D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006). [CrossRef]
  7. A. G. Marshall and M. B. Comisarow, “Fourier and Hadamard transform methods in spectroscopy,” Anal. Chem. 47, 491A–504A (1975). [CrossRef]
  8. J. A. Decker, “Experimental realization of the multiplex advantage with a Hadamard-transform spectrometer,” Appl. Opt. 10, 510–514 (1971). [CrossRef]
  9. D. J. Brady, “Multiplex sensors and the constant radiance theorem,” Opt. Lett. 27, 16–18 (2002). [CrossRef]
  10. M. A. Neifeld and P. Shankar, “Feature-specific imaging,” Appl. Opt. 42, 3379–3389 (2003). [CrossRef]
  11. M. Wakin, M. Duarte, S. Sarvotham, D. Baron, and R. Baraniuk, “Recovery of jointly sparse signals from few random projections,” in Advances in Neural Information Processing Systems 18, Y. Weiss, B. Schölkopf, and J. Platt, eds. (MIT Press, 2006), pp. 1433–1440.
  12. J. Haupt and R. Nowak, “Signal reconstruction from noisy random projections,” IEEE Trans. Inf. Theory 52, 4036–4048(2006). [CrossRef]
  13. 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]
  14. D. Dong and J. Atick, “Statistics of natural time-varying images,” Network Comput. Neural Syst. 6, 345–358 (1995). [CrossRef]
  15. T. Sikora, “MPEG digital video-coding standards,” IEEE Signal Process. Mag. 14, 82–100 (1997). [CrossRef]
  16. G. K. Wallace, “The JPEG still picture compression standard,” IEEE Trans. Consum. Electron. 38, xviii–xxxiv (1992). [CrossRef]
  17. R. J. Clarke, “Relation between the Karhunen-Loève and cosine transforms,” IEE Proc. Commun. Radar Signal Process.128, 359–360 (1981).
  18. D. S. Taubman and M. W. Marcellin, JPEG 2000: Image Compression Fundamentals, Standards and Practice (Kluwer Academic, 2001).
  19. S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Vol. I (Prentice-Hall, 1993).
  20. M. A. Neifeld and J. Ke, “Optical architectures for compressive imaging,” Appl. Opt. 46, 5293–5303 (2007). [CrossRef]
  21. E. R. Fossum, “Charge-coupled computing for focal plane image preprocessing,” Opt. Eng. 26, 916–922 (1987).
  22. E. R. Fossum, “Charge-domain analog signal processing for detector arrays,” Nucl. Instrum. Methods Phys. Res. A 275, 530–535 (1989). [CrossRef]
  23. E. R. Fossum, S. E. Kemeny, R. A. Bredthauer, and M. LaShell, “Digitally programmable gain control circuit for charge-domain signal processing,” IEEE J. Solid-State Circuits 26, 683–686 (1991). [CrossRef]

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