On Asymptotic Incoherence and Its Implications for Compressed Sensing of Inverse Problems

@article{Jones2016OnAI,
  title={On Asymptotic Incoherence and Its Implications for Compressed Sensing of Inverse Problems},
  author={A. Jones and B. Adcock and A. Hansen},
  journal={IEEE Transactions on Information Theory},
  year={2016},
  volume={62},
  pages={1020-1037}
}
  • A. Jones, B. Adcock, A. Hansen
  • Published 2016
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • Recently, it has been shown that incoherence is an unrealistic assumption for compressed sensing when applied to many inverse problems. Instead, the key property that permits efficient recovery in such problems is the so-called local incoherence. Similarly, the standard notion of sparsity is also inadequate for many real-world problems. In particular, in many applications, the optimal sampling strategy depends on asymptotic incoherence and the signal sparsity structure. The purpose of this… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 66 REFERENCES
    On asymptotic structure in compressed sensing
    59
    Stable and Robust Sampling Strategies for Compressive Imaging
    125
    On fundamentals of models and sampling in compressed sensing
    8
    A Probabilistic and RIPless Theory of Compressed Sensing
    462
    Generalized Sampling and Infinite-Dimensional Compressed Sensing
    117