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