• DocumentCode
    284989
  • Title

    Blind deconvolution of sparse spike trains using stochastic optimization

  • Author

    Goussard, Yves

  • Author_Institution
    Signal & Image Process. Inst., Univ. of Southern California, Los Angeles, CA, USA
  • Volume
    4
  • fYear
    1992
  • fDate
    23-26 Mar 1992
  • Firstpage
    593
  • Abstract
    The author proposes an approach to blind deconvolution of Bernoulli-Gaussian processes based upon true maximum-likelihood estimation of fixed-dimension quantities. The maximum likelihood estimator is implemented using a stochastic version of the expectation-maximization (EM) algorithm. Practical results are in accordance with the known behavior of maximum-likelihood estimates, and the algorithm presents a moderate numerical complexity
  • Keywords
    maximum likelihood estimation; signal processing; stochastic processes; Bernoulli-Gaussian processes; blind deconvolution; expectation-maximization; maximum-likelihood estimation; numerical complexity; sparse spike trains; stochastic optimization; Additive noise; Deconvolution; Image processing; Image restoration; Linear systems; Maximum likelihood estimation; Signal processing; Speech processing; Stochastic processes; Stochastic resonance;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on
  • Conference_Location
    San Francisco, CA
  • ISSN
    1520-6149
  • Print_ISBN
    0-7803-0532-9
  • Type

    conf

  • DOI
    10.1109/ICASSP.1992.226378
  • Filename
    226378
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=284989