Name
Abstract | ||
|---|---|---|
In this paper, we extend the idea of the seeding matrix design and introduce the modulated matrix framework for compressed sensing. The 1-D state evolution equation is derived to track the sample distortion performance as a function of the signal distribution and the rescaling matrix. A special example, the two-block matrix, is presented as a generalization of the hybrid zeroing matrix. The first order phase transition is further studied to better understand the dynamics. With the two-block matrix, exact recovery can be achieved in the region where the homogeneous Gaussian matrix is not optimal for the sparse signals. For compressible signals, the reconstruction quality can also be effectively improved. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6854017 | ICASSP |
Keywords | Field | DocType |
1d state evolution equation,homogeneous gaussian matrix,block state evolution equation,matrix algebra,phase transition,hybrid zeroing matrix,modulated measurement matrix design,reconstruction quality,sample distortion function,seeding matrix design,compressed sensing,gaussian processes,modulated matrix,signal reconstruction,signal distribution,sample distortion performance,two-block matrix,mathematical model,image reconstruction,sparse matrices | Generator matrix,Essential matrix,Mathematical optimization,Computer science,Eigendecomposition of a matrix,State-transition matrix,Band matrix,Compressed sensing,Sparse matrix,DFT matrix | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunli Guo | 1 | 26 | 3.82 |
| Mike E. Davies | 2 | 1664 | 120.39 |