### Abstract

Original language | English |
---|---|

Article number | e4 |

Number of pages | 84 |

Journal | Forum of Mathematics, Sigma |

Volume | 5 |

Early online date | 15 Feb 2017 |

DOIs | |

Publication status | Published - Feb 2017 |

### Cite this

*Forum of Mathematics, Sigma*,

*5*, [e4]. https://doi.org/10.1017/fms.2016.32

**Breaking the coherence barrier: A new theory for compressed sensing.** / Adcock, Ben; Hansen, Anders C; Poon, Clarice; Roman, Bogdan.

Research output: Contribution to journal › Conference article

*Forum of Mathematics, Sigma*, vol. 5, e4. https://doi.org/10.1017/fms.2016.32

}

TY - JOUR

T1 - Breaking the coherence barrier: A new theory for compressed sensing

AU - Adcock, Ben

AU - Hansen, Anders C

AU - Poon, Clarice

AU - Roman, Bogdan

PY - 2017/2

Y1 - 2017/2

N2 - This paper presents a framework for compressed sensing that bridges a gap between existing theory and the current use of compressed sensing in many real-world applications. In doing so, it also introduces a new sampling method that yields substantially improved recovery over existing techniques. In many applications of compressed sensing, including medical imaging, the standard principles of incoherence and sparsity are lacking. Whilst compressed sensing is often used successfully in such applications, it is done largely without mathematical explanation. The framework introduced in this paper provides such a justification. It does so by replacing these standard principles with three more general concepts: asymptotic sparsity, asymptotic incoherence and multilevel random subsampling. Moreover, not only does this work provide such a theoretical justification, it explains several key phenomena witnessed in practice. In particular, and unlike the standard theory, this work demonstrates the dependence of optimal sampling strategies on both the incoherence structure of the sampling operator and on the structure of the signal to be recovered. Another key consequence of this framework is the introduction of a new structured sampling method that exploits these phenomena to achieve significant improvements over current state-of-the-art techniques.

AB - This paper presents a framework for compressed sensing that bridges a gap between existing theory and the current use of compressed sensing in many real-world applications. In doing so, it also introduces a new sampling method that yields substantially improved recovery over existing techniques. In many applications of compressed sensing, including medical imaging, the standard principles of incoherence and sparsity are lacking. Whilst compressed sensing is often used successfully in such applications, it is done largely without mathematical explanation. The framework introduced in this paper provides such a justification. It does so by replacing these standard principles with three more general concepts: asymptotic sparsity, asymptotic incoherence and multilevel random subsampling. Moreover, not only does this work provide such a theoretical justification, it explains several key phenomena witnessed in practice. In particular, and unlike the standard theory, this work demonstrates the dependence of optimal sampling strategies on both the incoherence structure of the sampling operator and on the structure of the signal to be recovered. Another key consequence of this framework is the introduction of a new structured sampling method that exploits these phenomena to achieve significant improvements over current state-of-the-art techniques.

U2 - 10.1017/fms.2016.32

DO - 10.1017/fms.2016.32

M3 - Conference article

VL - 5

JO - Forum of Mathematics, Sigma

JF - Forum of Mathematics, Sigma

SN - 2050-5094

M1 - e4

ER -