Cosparsity in Compressed Sensing

  • Maryia Kabanava
  • Holger RauhutEmail author
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)


Analysis 1-recovery is a strategy of acquiring a signal, that is sparse in some transform domain, from incomplete observations. In this chapter we give an overview of the analysis sparsity model and present theoretical conditions that guarantee successful nonuniform and uniform recovery of signals from noisy measurements. We derive a bound on the number of Gaussian and subgaussian measurements by examining the provided theoretical guarantees under the additional assumption that the transform domain is generated by a frame, which means that there are just few nonzero inner products of a signal of interest with frame elements.


Compressed Sensing Gaussian Measurement Measurement Matrix Tangent Cone Tight Frame 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



M. Kabanava and H. Rauhut acknowledge support by the European Research Council through the grant StG 258926.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.RWTH Aachen University, Lehrstuhl C für Mathematik (Analysis)AachenGermany

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