Closely spaced object resolution using a quantum annealing model

J. J. Tran; R. F. Lucas; K. J. Scully; D. L. Semmen

doi:10.1117/12.2042604

2 February 2014 Closely spaced object resolution using a quantum annealing model

J. J. Tran, R. F. Lucas, K. J. Scully, D. L. Semmen

Proceedings Volume 9020, Computational Imaging XII; 90200D (2014) https://doi.org/10.1117/12.2042604
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

One of the challenges of automated target recognition and tracking on a two-dimensional focal plane is the ability to resolve closely spaced objects (CSO). To date, one of the best CSO-resolution algorithms first subdivides a cluster of image pixels into equally spaced grid points; then it conjectures that K targets are located at the centers of those sub-pixels and, for each set of such locations, calculates the associated irradiance values that minimizes the sum of squares of the residuals. The set of target locations that leads to the minimal residual becomes the initial starting point to a non-linear least-squares fit (e.g. Levenberg-Marquardt, Nelder-Mead, trust-region, expectation-maximization, etc.), which completes the estimation. The overall time complexity is exponential in K. Although numerous strides have been made over the years vis-`a-vis heuristic optimization techniques, the CSO resolution problem remains largely intractable, due to its combinatoric nature. We propose a novel approach to address this computational obstacle, employing a technique that maps the CSO resolution algorithm to a quantum annealing model which can then be programmed on an adiabatic quantum optimization device, e.g., the D-Wave architecture.

Citation Download Citation

J. J. Tran, R. F. Lucas, K. J. Scully, and D. L. Semmen "Closely spaced object resolution using a quantum annealing model", Proc. SPIE 9020, Computational Imaging XII, 90200D (2 February 2014); https://doi.org/10.1117/12.2042604

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available