title: Compressive blind image deconvolution.
abstract: We propose a novel blind image deconvolution (BID) regularization framework for compressive sensing (CS) based imaging systems capturing blurred images. The proposed framework relies on a constrained optimization technique, which is solved by a sequence of unconstrained sub-problems, and allows the incorporation of existing CS reconstruction algorithms in compressive BID problems. As an example, a non-convex lp quasi-norm with is employed as a regularization term for the image, while a simultaneous auto-regressive regularization term is selected for the blur. Nevertheless, the proposed approach is very general and it can be easily adapted to other state-of-the-art BID schemes that utilize different, application specific, image/blur regularization terms. Experimental results, obtained with simulations using blurred synthetic images and real passive millimeter-wave images, show the feasibility of the proposed method and its advantages over existing approaches.
authors.affiliation: Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208-3118, USA. amizic@northwestern.edu
authors.forename: Bruno
authors.initials: B
authors.lastname: Amizic
authors.forename: Leonidas
authors.initials: L
authors.lastname: Spinoulas
authors.forename: Rafael
authors.initials: R
authors.lastname: Molina
authors.forename: Aggelos K
authors.initials: AK
authors.lastname: Katsaggelos