• DocumentCode
    3063202
  • Title

    Optimal incorporation of sparsity information by weighted ℓ1 optimization

  • Author

    Tanaka, Toshiyuki ; Raymond, Jack

  • Author_Institution
    Grad. Sch. of Inf., Univ. of Kyoto, Kyoto, Japan
  • fYear
    2010
  • fDate
    13-18 June 2010
  • Firstpage
    1598
  • Lastpage
    1602
  • Abstract
    Compressed sensing of sparse sources can be improved by incorporating prior knowledge of the source. In this paper we demonstrate a method for optimal selection of weights in weighted ℓ1 norm minimization for a noiseless reconstruction model, and show the improvements in compression that can be achieved.
  • Keywords
    information theory; optimisation; compressed sensing; noiseless reconstruction model; norm minimization; optimal incorporation; optimization; sparsity information; Compressed sensing; Image coding; Image processing; Image reconstruction; Informatics; Lifting equipment; Minimization methods; Physics; Random variables; Topology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory Proceedings (ISIT), 2010 IEEE International Symposium on
  • Conference_Location
    Austin, TX
  • Print_ISBN
    978-1-4244-7890-3
  • Electronic_ISBN
    978-1-4244-7891-0
  • Type

    conf

  • DOI
    10.1109/ISIT.2010.5513420
  • Filename
    5513420
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3063202