Name
Abstract | ||
|---|---|---|
Block-sparse reconstruction, which arises from the reconstruction of block-sparse signals in structured compressed sensing, is generally considered difficult to solve due to the mixed-norm structure. In this letter, we propose an algorithm for reconstructing block-sparse signals, that is an extension of fixed point continuation in block-wise case by incorporating block coordinate descent technique. We also apply our algorithm to multiple measurement vector reconstruction, that is a special case of block-sparse reconstruction and can be used in magnetic resonance imaging reconstruction. Numerical results show the validity of our algorithm for both synthetic and real-world data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/LSP.2012.2195488 | IEEE Signal Process. Lett. |
Keywords | Field | DocType |
block-sparse reconstruction,block coordinate descent,structured compressed sensing,approximation theory,magnetic resonance imaging reconstruction,measurement vector reconstruction,block coordinate descent technique,compressed sensing,fixed point continuation,block-wise case,signal reconstruction,mixed-norm structure,block-sparse signal,block fixed point continuation algorithm,magnetic resonance imaging,magnetic resonance image,signal processing,vectors,image reconstruction,fixed point,minimization | Iterative reconstruction,Mathematical optimization,Algorithm,Approximation theory,Minification,Coordinate descent,Fixed point,Signal reconstruction,Mathematics,Compressed sensing,Special case | Journal |
Volume | Issue | ISSN |
19 | 6 | 1070-9908 |
Citations | PageRank | References |
8 | 0.50 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Zou | 1 | 16 | 3.00 |
| Yuli Fu | 2 | 200 | 29.90 |
| Shengli Xie | 3 | 2530 | 161.51 |