Back to Search
Start Over
Orthonormal Expansion \ell1-Minimization Algorithms for Compressed Sensing.
- Source :
-
IEEE Transactions on Signal Processing . Dec2011, Vol. 59 Issue 12, p6285-6290. 6p. - Publication Year :
- 2011
-
Abstract
- Compressed sensing aims at reconstructing sparse signals from significantly reduced number of samples, and a popular reconstruction approach is \ell1-norm minimization. In this correspondence, a method called orthonormal expansion is presented to reformulate the basis pursuit problem for noiseless compressed sensing. Two algorithms are proposed based on convex optimization: one exactly solves the problem and the other is a relaxed version of the first one. The latter can be considered as a modified iterative soft thresholding algorithm and is easy to implement. Numerical simulation shows that, in dealing with noise-free measurements of sparse signals, the relaxed version is accurate, fast and competitive to the recent state-of-the-art algorithms. Its practical application is demonstrated in a more general case where signals of interest are approximately sparse and measurements are contaminated with noise. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1053587X
- Volume :
- 59
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 67227247
- Full Text :
- https://doi.org/10.1109/TSP.2011.2168216