Journal of Mathematical Imaging and Vision

, Volume 48, Issue 2, pp 266–278 | Cite as

An Evaluation of the Sparsity Degree for Sparse Recovery with Deterministic Measurement Matrices

  • Y. Berthoumieu
  • C. Dossal
  • N. Pustelnik
  • P. Ricoux
  • F. Turcu
Article

Abstract

The paper deals with the estimation of the maximal sparsity degree for which a given measurement matrix allows sparse reconstruction through 1-minimization. This problem is a key issue in different applications featuring particular types of measurement matrices, as for instance in the framework of tomography with low number of views. In this framework, while the exact bound is NP hard to compute, most classical criteria guarantee lower bounds that are numerically too pessimistic. In order to achieve an accurate estimation, we propose an efficient greedy algorithm that provides an upper bound for this maximal sparsity. Based on polytope theory, the algorithm consists in finding sparse vectors that cannot be recovered by 1-minimization. Moreover, in order to deal with noisy measurements, theoretical conditions leading to a more restrictive but reasonable bounds are investigated. Numerical results are presented for discrete versions of tomography measurement matrices, which are stacked Radon transforms corresponding to different tomograph views.

Keywords

Compressed sensing Deterministic matrix Sparsity Greedy algorithm Sparcity degree 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Y. Berthoumieu
    • 1
  • C. Dossal
    • 2
  • N. Pustelnik
    • 3
  • P. Ricoux
    • 4
  • F. Turcu
    • 1
  1. 1.Institut polytechnique de BordeauxUniversité de Bordeaux, IMS, UMR CNRS 5218Talence cedexFrance
  2. 2.Université de BordeauxIMB, UMR CNRS 5584Talence cedexFrance
  3. 3.Laboratoire de Physique de l’ENS LyonUMR CNRS 5672LyonFrance
  4. 4.TOTAL S.A/DG/Direction ScientifiqueParis la Defense CedexFrance

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