@inproceedings{18332,
  abstract     = {Presented here is a generalization of the modified relative Newton method, recently proposed in [1] for quasi-maximum likelihood blind source separation. Special structure of the Hessian matrix allows to perform block-coordinate Newton descent, which significantly reduces the algorithm computational complexity and boosts its performance. Simulations based on artificial and real data show that the separation quality using the proposed algorithm outperforms other accepted blind source separation 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        = {406–413},
  publisher    = {Springer Nature},
  title        = {{Blind source separation using the block-coordinate relative Newton method}},
  doi          = {10.1007/978-3-540-30110-3_52},
  volume       = {3195},
  year         = {2004},
}

@inproceedings{18333,
  abstract     = {The relative Newton algorithm, previously proposed for quasi maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smooth approximation of the absolute value is used in modelling the log probability density function, which is suitable for sparse sources. We propose a method of sparsification, which allows blind deconvolution of sources with arbitrary distribution, and show how to find optimal sparsifying transformations by training.},
  author       = {Bronstein, Alexander and Bronstein, Michael M. and Zibulevsky, Michael and Zeevi, Yehoshua Y.},
  booktitle    = {5th International Conference on Independent Component Analysis and Blind Signal Separation},
  isbn         = {9783540230564},
  issn         = {1611-3349},
  location     = {Granada, Spain},
  pages        = {500--507},
  publisher    = {Springer Nature},
  title        = {{Optimal sparse representations for blind deconvolution of images}},
  doi          = {10.1007/978-3-540-30110-3_64},
  volume       = {3195},
  year         = {2004},
}

@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},
}