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Blind Deconvolution
Unsupervised Adaptive Filtering: Blind source separation. Simon S. A complete, one-stop reference on the state of the act of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel equalization algorithms.
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Unsupervised Adaptive Filtering, Volume 2, Blind Deconvolution
Toggle navigation. Blind Deconvolution Simon Haykin , Editor In the usual formulation of the deconvolution problem, it is assumed that the system input and system output are both known. There are, however, many important physical situations where it is impractical to assume the availability of the system input - situations of blind deconvolution. This book is devoted completely to the study of the blind deconvolution problem.
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