Optimally Tuned Iterative Reconstruction Algorithms for Compressed Sensing

@article{Maleki2010OptimallyTI,
  title={Optimally Tuned Iterative Reconstruction Algorithms for Compressed Sensing},
  author={Arian Maleki and David L. Donoho},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  year={2010},
  volume={4},
  pages={330-341}
}
We conducted an extensive computational experiment, lasting multiple CPU-years, to optimally select parameters for two important classes of algorithms for finding sparse solutions of underdetermined systems of linear equations. We make the optimally tuned implementations available at sparselab.stanford.edu; they run ¿out of the box¿ with no user tuning: it is not necessary to select thresholds or know the likely degree of sparsity. Our class of algorithms includes iterative hard and soft… CONTINUE READING
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Redundant multiscale transforms and their application for morphological component analysis

  • J. L. Starck, M. Elad, D. L. Donoho
  • J. Adv. Imaging Electron Phys., vol. 132, pp. 287…
  • 2004
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