• DocumentCode
    2053300
  • Title

    Statistical mechanics approach to sparse noise denoising

  • Author

    Vehkapera, Mikko ; Kabashima, Yoshiyuki ; Chatterjee, Saptarshi

  • Author_Institution
    ACCESS Linnaeus Centre, KTH R. Inst. of Technol., Stockholm, Sweden
  • fYear
    2013
  • fDate
    9-13 Sept. 2013
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Reconstruction fidelity of sparse signals contaminated by sparse noise is considered. Statistical mechanics inspired tools are used to show that the ℓ1-norm based convex optimization algorithm exhibits a phase transition between the possibility of perfect and imperfect reconstruction. Conditions characterizing this threshold are derived and the mean square error of the estimate is obtained for the case when perfect reconstruction is not possible. Detailed calculations are provided to expose the mathematical tools to a wide audience.
  • Keywords
    compressed sensing; image denoising; image reconstruction; mean square error methods; optimisation; statistical mechanics; convex optimization algorithm; mean square error; phase transition; reconstruction fidelity; sparse noise denoising; sparse signals; statistical mechanics; Abstracts; Multiaccess communication; Radio access networks; replica method; sparse signals and noise; statistical mechanical analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European
  • Conference_Location
    Marrakech
  • Type

    conf

  • Filename
    6811436
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2053300