Corpus ID: 10387505

Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing

  title={Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing},
  author={B. Adcock and A. Hansen and C. Poon and B. Roman},
  • B. Adcock, A. Hansen, +1 author B. Roman
  • Published 2013
  • Computer Science
  • ArXiv
  • We introduce a mathematical framework that bridges a substantial gap between compressed sensing theory and its current use in real-world applications. Although completely general, one of the principal applications for our framework is the Magnetic Resonance Imaging (MRI) problem. Our theory provides a comprehensive explanation for the abundance of numerical evidence demonstrating the advantage of so-called variable density sampling strategies in compressive MRI. Besides this, another important… CONTINUE READING
    62 Citations

    Figures and Topics from this paper

    Overcoming the coherence barrier in compressed sensing
    • 1
    • PDF
    On Asymptotic Incoherence and Its Implications for Compressed Sensing of Inverse Problems
    • 7
    • PDF
    Generalized sampling: stable reconstructions, inverse problems and compressed sensing over the continuum
    • 33
    • PDF
    Generalized Sampling and Infinite-Dimensional Compressed Sensing
    • 127
    • PDF
    Stable and Robust Sampling Strategies for Compressive Imaging
    • 137
    • PDF
    Structured random measurements in signal processing
    • 23
    • PDF
    Compressive Sensing with Orthonormal Measurements
    • PDF
    On Compressive orthonormal Sensing
    • Yi Zhou, Huishuai Zhang, Y. Liang
    • Mathematics, Computer Science
    • 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
    • 2016
    • 2
    • PDF


    On Variable Density Compressive Sampling
    • 81
    • PDF
    Structured Compressed Sensing: From Theory to Applications
    • 850
    • PDF
    A Probabilistic and RIPless Theory of Compressed Sensing
    • E. Candès, Y. Plan
    • Mathematics, Computer Science
    • IEEE Transactions on Information Theory
    • 2011
    • 439
    • PDF
    Model-Based Compressive Sensing
    • 1,515
    • PDF
    Introduction to compressed sensing
    • 325
    Sensitivity to Basis Mismatch in Compressed Sensing
    • 416
    Spread Spectrum Magnetic Resonance Imaging
    • 67
    • PDF
    Variable density compressed image sampling
    • 104
    • PDF
    Sparse MRI: The application of compressed sensing for rapid MR imaging
    • 5,016
    • PDF
    Compressive sensing
    • 1,431
    • PDF