We look at the design of projective measurements for compressive imaging based upon image priors and device constraints. If one assumes that image patches from natural imagery can be modeled as a low rank manifold, we develop an optimality criterion for a measurement matrix based upon separating the canonical elements of the manifold prior. We then characterize this manifold based upon prior training imagery under a treebased framework which can be implemented adaptively. We also illustrate how these adaptive measurements can incorporate prior knowledge regarding the constrains of the device being used to collect the measurements. Simulated performance results are presented and compared against a standard imaging paradigm as well as more conventional compressive imaging techniques.

Date Published: 29 April 2013
PDF: 11 pages
Proc. SPIE 8748, Optical Pattern Recognition XXIV, 874802 (29 April 2013); doi: 10.1117/12.2015453

Published in SPIE Proceedings Vol. 8748:
Optical Pattern Recognition XXIV
David Casasent; Tien-Hsin Chao, Editor(s)