  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.
