Using Correlated Subset Structure for Compressive Sensing Recovery

Authors

Atul Divekar, Alcatel-Lucent
Deanna Needell, Claremont McKenna CollegeFollow

Document Type

Article - postprint

Department

Mathematics (CMC)

Publication Date

6-10-2013

Abstract

Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the results in compressive sensing concern random sampling matrices such as Gaussian and Bernoulli matrices. In common physically feasible signal acquisition and reconstruction scenarios such as super-resolution of images, the sensing matrix has a non-random structure with highly correlated columns. Here we present a compressive sensing recovery algorithm that exploits this correlation structure. We provide algorithmic justification as well as empirical comparisons.

Rights Information

© 2013 SAMPTA

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Recommended Citation

Divekar, A., Needell, D., "Using Correlated Subset Structure for Compressive Sensing Recovery", Proc. 10th International Conf. on Sampling Theory and Applications (SAMPTA) 2013. http://arxiv.org/abs/1302.3918