On Variable Density Compressive Sampling

We advocate an optimization procedure for variable density sampling in the context of compressed sensing. In this perspective, we introduce a minimization problem for the coherence between the sparsity and sensing bases, whose solution provides an optimized sampling profile. This minimization problem is solved with the use of convex optimization algorithms. We also propose a refinement of our technique when prior information is available on the signal support in the sparsity basis. The effectiveness of the method is confirmed by numerical experiments. Our results also provide a theoretical underpinning to state-of-the-art variable density Fourier sampling procedures used in magnetic resonance imaging. Code: http://lts2www.epfl.ch/people/gilles/softwares


Published in:
IEEE Signal Processing Letters, 18, 10, 595-598
Year:
2011
Publisher:
Institute of Electrical and Electronics Engineers
ISSN:
1070-9908
Keywords:
Laboratories:




 Record created 2011-05-02, last modified 2018-01-28

External link:
Download fulltext
Publisher's version
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)