Compressive blind image deconvolution

Bruno Amizic, Leonidas Spinoulas, Rafael Molina, Aggelos K. Katsaggelos

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

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 l p quasi-norm with 0 < p < 1 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-ofthe- 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.

Original languageEnglish (US)
Article number6523098
Pages (from-to)3994-4006
Number of pages13
JournalIEEE Transactions on Image Processing
Volume22
Issue number10
DOIs
StatePublished - Sep 17 2013

Keywords

  • Blind image deconvolution
  • Compressive sensing
  • Constrained optimization
  • Inverse methods

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Compressive blind image deconvolution'. Together they form a unique fingerprint.

Cite this