A thin, agile multiresolution, computational imaging sensor architecture, termed PANOPTES (processing arrays of Nyguist-limited observations to produce a thin electro-optic sensor), which utilizes arrays of microelectromechanical mirrors to adaptively redirect the fields of view of multiple low-resolution subimagers, is described. An information theory-based algorithm adapts the system and restores the image. The modulation transfer function (MTF) effects of utilizing micromirror arrays to steering imaging systems are analyzed, and computational methods for combining data collected from systems with differing MTFs are presented.
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 systems shows the advantage of joint optimization and concurrent design.
In this paper, we propose a novel method for the optimal co-design of the optical and reconstruction filters in a computational imaging system. Closed form solutions are presented for design of optimal observation matrix for a fixed reconstruction matrix. An iterative method for computing global optimal filters is then proposed based on the derived analytical solution and the well known Wiener filter. The performance of the proposed optimal filters represents a universal bound on the performance of any physically realizable computational imaging system.
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