• DocumentCode
    719234
  • Title

    Nonuniform sparse recovery with random convolutions

  • Author

    James, David ; Rauhut, Holger

  • Author_Institution
    Inst. for Numerical & Appl. Math., Univ. of Goettingen, Goettingen, Germany
  • fYear
    2015
  • fDate
    25-29 May 2015
  • Firstpage
    34
  • Lastpage
    38
  • Abstract
    We discuss the use of random convolutions for Compressed Sensing applications. In particular, we will show that after convolving an N-dimensional, s-sparse signal with a Rademacher or Steinhaus sequence, it can be recovered via l1-minimization using only m ≳ s log(N/ε) arbitrary chosen samples with probability at least 1 - ε.
  • Keywords
    compressed sensing; convolution; probability; N-dimensional s-sparse signal; Rademacher sequence; Steinhaus sequence; compressed sensing applications; nonuniform sparse recovery; random convolutions; Compressed sensing; Convolution; Electronic mail; Random variables; Sparse matrices; Upper bound;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Sampling Theory and Applications (SampTA), 2015 International Conference on
  • Conference_Location
    Washington, DC
  • Type

    conf

  • DOI
    10.1109/SAMPTA.2015.7148845
  • Filename
    7148845
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=719234