Name
Abstract | ||
|---|---|---|
Modern data is customarily of multimodal nature, and analysis tasks typically require separation into the single components. Although a highly ill-posed problem, the morphological difference of these components sometimes allow a very precise separation such as, for instance, in neurobiological imaging a separation into spines (pointlike structures) and dendrites (curvilinear structures). Recently, applied harmonic analysis introduced powerful methodologies to achieve this task, exploiting specifically designed representation systems in which the components are sparsely representable, combined with either performing ℓ1 minimization or thresholding on the combined dictionary. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.acha.2013.02.001 | Applied and Computational Harmonic Analysis |
Keywords | Field | DocType |
Thresholding,Sparse representation,Mutual coherence,Tight frames,Curvelets,Shearlets,Radial wavelets,Wavefront set | Wavefront,Mathematical analysis,Index set,Coherence (physics),Curvilinear coordinates,Thresholding,Mathematics,Microlocal analysis,Curvelet,Wavelet | Journal |
Volume | Issue | ISSN |
36 | 1 | 1063-5203 |
Citations | PageRank | References |
2 | 0.44 | 16 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gitta Kutyniok | 1 | 325 | 34.77 |