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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: James C. Wyant
  • Vol. 47, Iss. 25 — Sep. 1, 2008
  • pp: 4457–4471

Compressive imaging system design using task-specific information

Amit Ashok, Pawan K. Baheti, and Mark A. Neifeld  »View Author Affiliations


Applied Optics, Vol. 47, Issue 25, pp. 4457-4471 (2008)
http://dx.doi.org/10.1364/AO.47.004457


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Abstract

We present a task-specific information (TSI) based framework for designing compressive imaging (CI) systems. The task of target detection is chosen to demonstrate the performance of the optimized CI system designs relative to a conventional imager. In our optimization framework, we first select a projection basis and then find the associated optimal photon-allocation vector in the presence of a total photon-count constraint. Several projection bases, including principal components (PC), independent components, generalized matched-filter, and generalized Fisher discriminant (GFD) are considered for candidate CI systems, and their respective performance is analyzed for the target-detection task. We find that the TSI-optimized CI system design based on a GFD projection basis outperforms all other candidate CI system designs as well as the conventional imager. The GFD-based compressive imager yields a TSI of 0.9841 bits (out of a maximum possible 1 bit for the detection task), which is nearly ten times the 0.0979 bits achieved by the conventional imager at a signal-to-noise ratio of 5.0. We also discuss the relation between the information-theoretic TSI metric and a conventional statistical metric like probability of error in the context of the target-detection problem. It is shown that the TSI can be used to derive an upper bound on the probability of error that can be attained by any detection algorithm.

© 2008 Optical Society of America

OCIS Codes
(110.2970) Imaging systems : Image detection systems
(110.3000) Imaging systems : Image quality assessment
(200.3050) Optics in computing : Information processing
(200.4740) Optics in computing : Optical processing

ToC Category:
Imaging Systems

History
Original Manuscript: January 31, 2008
Revised Manuscript: May 31, 2008
Manuscript Accepted: July 3, 2008
Published: August 21, 2008

Citation
Amit Ashok, Pawan K. Baheti, and Mark A. Neifeld, "Compressive imaging system design using task-specific information," Appl. Opt. 47, 4457-4471 (2008)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-47-25-4457


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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] [PubMed]
  2. A. Ashok and M. A. Neifeld, “Pseudorandom phase masks for superresolution imaging from subpixel shifting,” Appl. Opt. 46, 2256-2268 (2007). [CrossRef] [PubMed]
  3. M. D. Stenner, A. Ashok, and M. A. Neifeld, “Multi-domain optimization for ultra-thin cameras,” in Frontiers in Optics (2006), paper FWH5.
  4. M. A. Neifeld, “Multi-domain optimization,” http://ocpl.ece.arizona.edu/mdo/.
  5. M. A. Neifeld and P. Shankar, “Feature-specific imaging,” Appl. Opt. 42, 3379-3389 (2003). [CrossRef] [PubMed]
  6. M. A. Neifeld and J. Ke, “Optical architectures for compressive imaging,” Appl. Opt. 46, 5293-5303 (2007). [CrossRef] [PubMed]
  7. M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cogn. Neurosci. 3, 71-86 (1991). [CrossRef]
  8. P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pat. Anal. Mach. Intell. 19, 711-720 (1997). [CrossRef]
  9. M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Trans. Neural Netw. 13, 1450-1464 (2002). [CrossRef]
  10. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (Wiley Interscience, 2000).
  11. H. Pal and M. A. Neifeld, “Multispectral principal component imaging,” Opt. Express 11, 2118-2125 (2003). [CrossRef] [PubMed]
  12. D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289-1306 (2006). [CrossRef]
  13. M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI [a look at how CS can improve on current imaging techniques],” IEEE Signal Process. Mag. 25 (2), 72-82 (2008 [CrossRef]
  14. A. Mahalanobis, “Optical systems for task specific compressed sensing and image reconstruction,” in Annual Meeting of the IEEE Lasers and Electro-Optics SocietyAnnual Meeting of the IEEE Lasers and Electro-Optics Society (IEEE, 2007), pp. 157-158. [CrossRef]
  15. M. A. Neifeld, A. Ashok, and P. K. Baheti, “Task specific information for imaging system analysis,” J. Opt. Soc. Am. A 24, 25-41 (2007). [CrossRef]
  16. M. F. Duarte, M. A. Davenport, M. B. Wakin, and R. G. Baraniuk, “Sparse signal detection from incoherent projections,” in Vol. 3 of Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP, 2006), pp. 14-19.
  17. D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 43-52 (2006).
  18. T. Cover and J. Thomas, Elements of Information Theory (Wiley, 1991). [CrossRef]
  19. D. Guo, S. Shamai, and S. Verdu, “Mutual information and minimum mean-square error in Gaussian channels,” IEEE Trans. Inf. Theory 51, 1261-1282 (2005). [CrossRef]
  20. D. P. Palomar and S. Verdu, “Gradient of mutual information in linear vector Gaussian channels,” IEEE Trans. Inf. Theory 52, 141-154 (2006). [CrossRef]
  21. D. P. Palomar and S. Verdu, “Representation of mutual information via input estimates,” IEEE Trans. Inf. Theory 53, 453-470 (2007). [CrossRef]
  22. N. Towghi and B. Javidi, “Optimum receivers for pattern recognition in the presence of Gaussian noise with unknown statistics,” J. Opt. Soc. Am. A 18, 1844-1852 (2001). [CrossRef]
  23. R. Patnaik and D. Casasent, “MINACE filter classification algorithms for ATR using MSTAR data,” Proc. SPIE 5807, 100-111 (2005). [CrossRef]
  24. R. Patnaik and D. Casasent, “SAR classification and confuser and clutter rejection tests on MSTAR ten-class data using Minace filters,” Proc. SPIE 6574, 657402 (2007). [CrossRef]
  25. J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, 1996), Chap. 7.
  26. M. Tanner, Tools for Statistical Inference, 2nd ed. (Springer, 1993).
  27. W. Gander and W. Gautschi, “Adaptive quadrature--revisited,” BIT 40, 84-101 (2000). [CrossRef]
  28. W. Wenzel and K. Hamacher, “A Stochastic tunneling approach for global minimization,” Phys. Rev. Lett. 82, 3003-3007 (1999). [CrossRef]
  29. I. T. Jolliffe, Principal Component Analysis (Springer, 2002).
  30. D. Barber and F. V. Agakov, “The IM algorithm: a variational approach to information maximization,” in NIPS (MIT Press, 2003).
  31. S. Kay, Fundamentals of Statistical Signal Processing: Detection Theory (Prentice-Hall, 1993).
  32. A. J. Bell and T. J. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution,” Neural Comput. 7, 1129-1159 (1995). [CrossRef] [PubMed]
  33. A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis (Wiley, 2001). [CrossRef]

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