Multi-dimensional sparse structured signal approximation using split Bregman iterations
This work addresses the challenge of structured sparse approximation for multi-dimensional signals, which is incremental as it builds on existing split Bregman methods.
The paper tackled the problem of sparse approximation for multi-dimensional signals while preserving prior structure, using a multi-dimensional split Bregman optimization approach, and conducted an empirical evaluation comparing it to state-of-the-art methods based on signal features.
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.