On Variable Density Compressive Sampling

Details On Variable Density Compressive Sampling On Variable Density Compressive Sampling

2011-09-28

arxiv.org/abs/1109.6202v1

IEEE Signal Processing Letters, vol. 18(10), pp. 595-598, 2011

Computer Science

Information Theory

Scientific paper

10.1109/LSP.2011.2163712

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.

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