@inproceedings{18334,
  abstract     = {We propose a relative optimization framework for quasi maximum likelihood blind deconvolution and the relative Newton method as its particular instance. Special Hessian structure allows its fast approximate construction and inversion with complexity comparable to that of gradient methods. The use of rational IIR restoration kernels provides a richer family of filters than the traditionally used FIR kernels. Smoothed absolute value and the smoothed deadzone functions allow accurate and robust deconvolution of super- and sub-Gaussian sources, respectively. Simulation results demonstrate the efficiency of the proposed methods.},
  author       = {Bronstein, Alexander and Bronstein, Michael M. and Zibulevsky, Michael},
  booktitle    = {5th International Conference on Independent Component Analysis and Blind Signal Separation},
  isbn         = {9783540230564},
  issn         = {1611-3349},
  location     = {Granada, Spain},
  pages        = {554–561},
  publisher    = {Springer Nature},
  title        = {{Blind deconvolution using the relative Newton method}},
  doi          = {10.1007/978-3-540-30110-3_71},
  volume       = {3195},
  year         = {2004},
}

@inproceedings{18335,
  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.},
  author       = {Bronstein, Alexander and Bronstein, Michael M. and Zibulevsky, Michael and Zeevi, Yehoshua Y.},
  booktitle    = {Proceedings of the 5th International Conference on Independent Component Analysis and Blind Signal Separation},
  isbn         = {9783540230564},
  issn         = {0302-9743},
  location     = {Granada, Spain},
  pages        = {677–684},
  publisher    = {Springer Nature},
  title        = {{QML blind deconvolution: Asymptotic analysis}},
  doi          = {10.1007/978-3-540-30110-3_86},
  volume       = {3195},
  year         = {2004},
}