Name
Abstract | ||
|---|---|---|
Recent works in modified compressed sensing (CS) show that reconstruction of sparse or compressible signals with partially known support yields better results than traditional CS. In this paper, we extend the ideas of these works to modify three iterative algorithms to incorporate the known support in the recovery process. The performance and effect of the prior information are studied through simulations. Results show that the modification of iterative algorithms improves their performance, needing fewer samples to yield an approximate reconstruction. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICASSP.2010.5495901 | 2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING |
Keywords | Field | DocType |
Compressed sensing, sampling methods, signal reconstruction, estimation | Least squares,Signal processing,Computer science,Artificial intelligence,Compressed sensing,Iterative reconstruction,Mathematical optimization,Pattern recognition,Iterative method,Data acquisition,Image coding,Algorithm,Signal reconstruction | Conference |
ISSN | Citations | PageRank |
1520-6149 | 29 | 1.49 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael E. Carrillo | 1 | 250 | 15.90 |
| Luisa F. Polania | 2 | 131 | 9.54 |
| Kenneth E. Barner | 3 | 812 | 70.19 |