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. © Springer-Verlag 2004.
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CITATION STYLE
Bronstein, A. M., Bronstein, M. M., Zibulevsky, M., & Zeevi, Y. Y. (2004). QML blind deconvolution: Asymptotic analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3195, 677–684. https://doi.org/10.1007/978-3-540-30110-3_86
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