Name
Abstract | ||
|---|---|---|
•Analysis of problems in which the sensor cannot be designed at will, but merely constrained by the outside world.•Analysis of solutions to problems where the signals are dense / high-complexity.•Analysis of the stability of the recovery algorithms based on (local) sparse approximation.•The method looks at sparsity in (potentially overlapping) subspaces and not at the sparsity of active subspaces.•The approaches are assessed numerically and all numerics are readily available. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.sigpro.2020.107615 | Signal Processing |
Keywords | Field | DocType |
Compressed sensing,Fusion frame,Sparse signal approximation | Signal processing,Robustness (computer science),Harmonic analysis,Artificial intelligence,Fuse (electrical),Compressed sensing,Computer vision,Mathematical optimization,Algorithm,Filter (signal processing),Fusion frame,Mathematics,Computational complexity theory | Journal |
Volume | ISSN | Citations |
174 | 0165-1684 | 1 |
PageRank | References | Authors |
0.36 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roza Aceska | 1 | 1 | 0.36 |
| Jean-Luc Bouchot | 2 | 12 | 1.72 |
| Shidong Li | 3 | 17 | 5.07 |