Name
Abstract | ||
|---|---|---|
This letter presents a new sparsity basis selection compressed sensing method (SBSCS) for improving signal reconstruction from compressed sensing (CS) measurements. Based on the observation that different classes of transform cause different sparsity expressions and better sparsity expression leads to better signal recovery, the proposed SBSCS method searches the best class of transform and basis in a set of redundant tree-structured dictionaries by nesting sparsity maximization within the CS minimization. The SBSCS method adaptively selects the class of transform and basis with the best sparsity measure at each ℓ1 iteration and converges quickly to the final class of transform and basis. Numerical experiments show that the proposed SBSCS method improves the quality of signal recovery over the existing best basis compressed sensing method (BBCS) proposed by Peyré in 2010. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/LSP.2015.2429748 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Basis selection, compressed sensing (CS), sparsity maximization, sparsity | Mathematical optimization,Basis selection,Pattern recognition,Expression (mathematics),Iterative method,Minification,Artificial intelligence,Numerical analysis,Signal reconstruction,Mathematics,Maximization,Compressed sensing | Journal |
Volume | Issue | ISSN |
22 | 10 | 1070-9908 |
Citations | PageRank | References |
4 | 0.40 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongjie Bi | 1 | 24 | 5.28 |
| Yongle Xie | 2 | 12 | 2.37 |
| Xifeng Li | 3 | 27 | 6.36 |
| Yahong Rosa Zheng | 4 | 885 | 76.15 |