Name
Abstract | ||
|---|---|---|
In cosparse analysis compressive sensing (CS), one seeks to estimate a non-sparse signal vector from noisy sub-Nyquist linear measurements by exploiting the knowledge that a given linear transform of the signal is cosparse, i.e., has sufficiently many zeros. We propose a novel approach to cosparse analysis CS based on the generalized approximate message passing (GAMP) algorithm. Unlike other AMP-based approaches to this problem, ours works with a wide range of analysis operators and regularizers. In addition, we propose a novel l(0)-like soft-thresholder based on MMSE denoising for a spike-and-slab distribution with an infinite-variance slab. Numerical demonstrations on synthetic and practical datasets demonstrate advantages over existing AMP-based, greedy, and reweighted-l(1) approaches. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) | Approximate message passing, belief propagation, compressed sensing |
Field | DocType | ISSN |
Noise reduction,Mathematical optimization,MATLAB,Pattern recognition,Computer science,Linear transform,Artificial intelligence,Operator (computer programming),Message passing,Compressed sensing,Belief propagation | Conference | 1520-6149 |
Citations | PageRank | References |
6 | 0.54 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Borgerding | 1 | 49 | 2.19 |
| Philip Schniter | 2 | 1620 | 93.74 |
| Jeremy P. Vila | 3 | 106 | 4.38 |
| Sundeep Rangan | 4 | 3101 | 163.90 |