Compressed Sensing with Coherent and Redundant Dictionaries

TL;DR

This paper demonstrates that accurate signal recovery from undersampled data is possible using L1-analysis even when signals are sparse in highly coherent, overcomplete dictionaries, expanding compressed sensing applicability.

Contribution

It introduces a new measurement matrix condition that guarantees recovery without requiring dictionary incoherence, addressing a key gap in compressed sensing theory.

Findings

Recovery is guaranteed under a generalized restricted isometry property.

L1-analysis effectively recovers signals sparse in redundant dictionaries.

Results apply to practical scenarios with highly coherent, overcomplete dictionaries.

Abstract

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.