Work

On Variable Density Compressive Sampling

Year: 2011

Type: article

Abstract: Incoherence between sparsity basis and sensing basis is an essential concept for compressive sampling. In this context, we advocate a coherence-driven optimization procedure for variable density sampling. The associated minimization problem is solved by 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 MRI.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2011.2163712

Authors: Gilles Puy, Pierre Vandergheynst, Yves Wiaux

Institutions: École Polytechnique Fédérale de Lausanne, University of Geneva

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, École Polytechnique Fédérale de Lausanne, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Centre d'Imagerie BioMédicale

Awards: Advanced Wavelet Solutions for Functional Imaging, G580951548