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QML Blind Deconvolution: Asymptotic Analysis

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Independent Component Analysis and Blind Signal Separation (ICA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3195))

Abstract

Blind deconvolution is considered as a problem of quasi maximum likelihood (QML) estimation of the restoration kernel. Simple closed-form expressions for the asymptotic estimation error are derived. The asymptotic performance bounds coincide with the Cramér-Rao bounds, when the true ML estimator is used. Conditions for asymptotic stability of the QML estimator are derived. Special cases when the estimator is super-efficient are discussed.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Technion – Israel Institute of Technology, 32000, Haifa, Israel

    Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky & Yehoshua Y. Zeevi

Authors
  1. Alexander M. Bronstein
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  2. Michael M. Bronstein
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  3. Michael Zibulevsky
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  4. Yehoshua Y. Zeevi
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Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet  & Alberto Prieto  & 

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Bronstein, A.M., Bronstein, M.M., Zibulevsky, M., Zeevi, Y.Y. (2004). QML Blind Deconvolution: Asymptotic Analysis. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_86

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_86

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

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