Compressive sensing by learning a Gaussian mixture model from measurements
Compressive sensing of signals drawn from a Gaussian mixture model (GMM) admits closed-form minimum mean squared error reconstruction from incomplete linear measurements. An accurate GMM signal model is usually not available a priori, because it is difficult to obtain training signals that match the...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 24(2015), 1 vom: 12. Jan., Seite 106-19
|
|---|
| 1. Verfasser: |
Yang, Jianbo
(VerfasserIn) |
|---|
| Weitere Verfasser: |
Liao, Xuejun,
Yuan, Xin,
Llull, Patrick,
Brady, David J,
Sapiro, Guillermo,
Carin, Lawrence |
|---|
| Format: | Online-Aufsatz
|
|---|
| Sprache: | English |
|---|
| Veröffentlicht: |
2015
|
|---|
| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|
|---|
| Schlagworte: | Journal Article
Research Support, U.S. Gov't, Non-P.H.S. |
|---|