Compressed Sensing with Coherent and Redundant Dictionaries

Details Compressed Sensing with Coherent and Redundant Dictionaries Compressed Sensing with Coherent and Redundant Dictionaries

2010-05-14

arxiv.org/abs/1005.2613v3

Mathematics

Numerical Analysis

Scientific paper

This article presents novel results concerning the recovery of signals from undersampled data in the common situation where such signals are not sparse in an orthonormal basis or incoherent dictionary, but in a truly redundant dictionary. This work thus bridges a gap in the literature and shows not only that compressed sensing is viable in this context, but also that accurate recovery is possible via an L1-analysis optimization problem. We introduce a condition on the measurement/sensing matrix, which is a natural generalization of the now well-known restricted isometry property, and which guarantees accurate recovery of signals that are nearly sparse in (possibly) highly overcomplete and coherent dictionaries. This condition imposes no incoherence restriction on the dictionary and our results may be the first of this kind. We discuss practical examples and the implications of our results on those applications, and complement our study by demonstrating the potential of L1-analysis for such problems.

Affiliated with

Also associated with

No associations

Rating

Compressed Sensing with Coherent and Redundant Dictionaries does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Compressed Sensing with Coherent and Redundant Dictionaries, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Compressed Sensing with Coherent and Redundant Dictionaries will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-520425