Paper Info
Title
Compressed Sensing With General Frames via Optimal-Dual-Based $\ell _{1}$ -Analysis
Abstract
Compressed sensing with sparse frame representations is seen to have much greater range of practical applications than that with orthonormal bases. In such settings, one approach to recover the signal is known as $\ell _{1}$-analysis. We expand in this paper the performance analysis of this approach by providing a weaker recovery condition than existing results in the literature. Our analysis is also broadly based on general frames and alternative dual frames (as analysis operators). As one application to such a general-dual-based approach and performance analysis, an optimal-dual-based technique is proposed to demonstrate the effectiveness of using alternative dual frames as analysis operators. An iterative algorithm is outlined for solving the optimal-dual-based $\ell _{1}$-analysis problem. The effectiveness of the proposed method and algorithm is demonstrated through several experiments.
Year
DOI
Venue
2011
10.1109/TIT.2012.2191612
IEEE Transactions on Information Theory
Keywords
DocType
Volume
algorithm design,compressed sensing,iterative methods,iterative algorithm,signal reconstruction,sparse matrices
Journal
58
Issue
ISSN
Citations 
7
IEEE Transactions on Information Theory, vol. 58, no. 7, pp. 4201-4214, July, 2012
2
PageRank 
References 
Authors
0.38
0
3
Name
Order
Citations
PageRank
Yulong Liu194.92
Tiebin Mi222.41
Shidong Li363.58