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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN 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)
http://dx.doi.org/10.1364/AO.47.000B86


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Abstract

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

History
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

Citation
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)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-47-10-B86


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