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Applied Optics

Applied Optics


  • Editor: James C. Wyant
  • Vol. 47, Iss. 10 — Apr. 1, 2008
  • pp: B86–B103

Computational imaging systems: joint design and end-to-end optimality

Tejaswini Mirani, Dinesh Rajan, Marc P. Christensen, Scott C. Douglas, and Sally L. Wood  »View Author Affiliations

Applied Optics, Vol. 47, Issue 10, pp. B86-B103 (2008)

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A framework is proposed for optimal joint design of the optical and reconstruction filters in a computational imaging system. First, a technique for the design of a physically unconstrained system is proposed whose performance serves as a universal bound on any realistic computational imaging system. Increasing levels of constraints are then imposed to emulate a physically realizable optical filter. The proposed design employs a generalized Benders’ decomposition method to yield multiple globally optimal solutions to the nonconvex optimization problem. Structured, closed-form solutions for the design of observation and reconstruction filters, in terms of the system input and noise autocorrelation matrices, are presented. Numerical comparison with a state-of-the-art optical system shows the advantage of joint optimization and concurrent design.

© 2008 Optical Society of America

OCIS Codes
(110.1758) Imaging systems : Computational imaging
(110.3010) Imaging systems : Image reconstruction techniques

ToC Category:
Imaging Systems

Original Manuscript: September 11, 2007
Revised Manuscript: January 10, 2008
Manuscript Accepted: January 25, 2008
Published: March 19, 2008

Virtual Issues
Vol. 3, Iss. 5 Virtual Journal for Biomedical Optics

Tejaswini Mirani, Dinesh Rajan, Marc P. Christensen, Scott C. Douglas, and Sally L. Wood, "Computational imaging systems: joint design and end-to-end optimality," Appl. Opt. 47, B86-B103 (2008)

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  1. J. Mait, “A history of imaging: revisiting the past to chart the future,” Opt. Photon. News 17, 22-27 (2006). [CrossRef]
  2. J. Mait, R. Athale, and J. van der Gracht, “Evolutionary paths in imaging and recent trends,” Opt. Express 11, 2093-2101(2003). [CrossRef] [PubMed]
  3. T. Cathey and E. Dowski, “New paradigm for imaging systems,” Appl. Opt. 41, 6080-6092 (2002). [CrossRef] [PubMed]
  4. J. Tanida, T. Kumagai, K. Yamada, S. Miyatake, K. Ishida, T. Morimoto, N. Kondou, D. Miyazaki, and Y. Ichioka, “Thin observation module by bound optics TOMBO: concept and experimental verification,” Appl. Opt. 40, 1806 (2001). [CrossRef]
  5. F. Russell and J. Goodman, “Nonredundant arrays and postdetection processing for aberration compensation in incoherent imaging,” J. Opt. Soc. Am. 61, 182-191 (1971). [CrossRef]
  6. J. Duparré, P. Schreiber, P. Dannberg, T. Scharf, P. Pelli, R. Völkel, H. Herzig, and A. Bräuer, “Artificial compound eyes--different concepts and their application to ultraflat image acquisition sensors,” Proc. SPIE 5346, 89-100 (2004). [CrossRef]
  7. E. Dowski and T. Cathey, “Extended depth of field through wave-front coding,” Appl. Opt. 34, 1859-1866 (1995). [CrossRef] [PubMed]
  8. M. Christensen, M. Haney, D. Rajan, S. Wood, and S. Douglas, “Panoptes: A thin agile multi-resolution imaging sensor,” presented at Government Microcircuit Applications and Critical Technology Conference (GOMACTech-05), 4-7 April 2005.
  9. M. Christensen, V. Bhakta, D. Rajan, T. Mirani, S. Douglas, S. Wood, and M. Haney, “Adaptive flat multiresolution multiplexed computational imaging architecture utilizing micromirror arrays to steer subimager fields of view,” Appl. Opt. 45, 2884-2892 (2006). [CrossRef] [PubMed]
  10. T. Mirani, M. Christensen, S. Douglas, D. Rajan, and S. Wood, “Optimal co-design of computational imaging systems,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings (ICASSP '05) (IEEE, 2005). Vol. 2, pp. 597-600. [CrossRef]
  11. A. Geoffrion, “Generalized benders decomposition,” J. Optim. Theory Appl. 10, 237-260 (1972).
  12. S. Boyd and L. Vandenberghe, Convex Optimization (Cambridge U. Press, 2003).
  13. D. Luenberger, Linear and Non-Linear Programming, 2nd ed. (Springer, 2004).
  14. K. Diamantaras and S. Kung, Principal Component Neural Networks: Theory and Applications (Wiley, 1996).
  15. G. Strang, Introduction to Linear Algebra, 3rd ed. (Wellesley-Cambridge Press, 2003).
  16. G. Golub and C. Loan, Matrix Computations, 3rd ed. (Johns Hopkins U. Press, 1996).
  17. H. Andrews and B. Hunt, Digital Image Restoration (Prentice-Hall, 1977).
  18. R. Gonzalez and R. Woods, Digital Image Processing, 2nd ed. (Prentice Hall, 2002).
  19. P. K. Baheti and M. A. Neifeld, “Feature-specific structured imaging,” Appl. Opt. 45, 7382-7391 (2006). [CrossRef] [PubMed]
  20. A. Geoffrion, “Elements of large-scale mathematical programming,” Manage. Sci. 16, 652-691 (1970). [CrossRef]
  21. J. A. O'Sullivan, “Alternating minimization algorithms: from Blahut-Arimoto to expectation-maximization,” in Codes, Curves, and Signals: Common Threads in Communications, A. Vardy, ed. (Springer, 1998), pp. 173-192. [CrossRef]
  22. J. Goodman, Introduction to Fourier Optics, 2nd ed. (McGraw-Hill, 1996).
  23. H. Faraji and W. James MacLean, “CCD noise removal in digital images,” IEEE Trans. Image Process. 15, (2006). [CrossRef]
  24. M. Brookes, The Matrix Reference Manual (Imperial College, 2005), http://www.ee.ic.ac.uk/hp/staff/dmb/matrix/intro.html#Intro.

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