Multi-dimensional sparse structured signal approximation using split Bregman iterations

Abstract

The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlying optimization problem is tackled using a multi-dimensional split Bregman optimization approach. An extensive empirical evaluation shows how the proposed approach compares to the state of the art depending on the signal features.

5 pages, ICASSP 2013 preprint

References in corpus (3)

• Structured Variable Selection with Sparsity-Inducing Norms
• Learning with Structured Sparsity
• An ADMM Algorithm for a Class of Total Variation Regularized Estimation Problems