Implementation: On Variable Density Compressive Sampling

Gilles Puy just tweeted the release of the code implementing the method described in the paper "On Variable Density Compressive Sampling" which we featured a year ago. From the page:

On Variable Density Compressive Sampling

Abstract: 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: VDS_code.zip or VDS_code.tar.gz.


If you use this code, please cite the following paper: Puy et al., "On Variable Density Compressive Sampling," in IEEE Signal Processing Letters, vol. 18(10), pp. 595-598, 2011.


Acknowledgement: This code make use of the function "oneProjectorMex" of the spgl1 toolbox. The SPGL1 toolbox is available at http://www.cs.ubc.ca/~mpf/spgl1/. The description of the theory of the SPGL1 algorithm is outlined in E. van den Berg and M. P. Friedlander, "Probing the Pareto frontier for basis pursuit solutions," SIAM J. on Scientific Computing, 31(2):890-912, 2008.

Comments: gilles {DOT} puy {AT} epfl {DOT} ch
Link to preprint
Link to published paper

Image Credit: NASA/JPL/Space Science Institute
W00075164.jpg was taken on August 20, 2012 and received on Earth August 22, 2012. The camera was pointing toward SATURN at approximately 1,483,173 miles (2,386,936 kilometers) away, and the image was taken using the CB2 and CL2 filters.

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