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Bayesian iterative method for blind deconvolution

机译:盲反卷积的贝叶斯迭代方法

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Abstract: Blind deconvolution is a typical solution to unknown LSI system inversion problems. When only the output is available, second order statistics are not sufficient to retrieve the phase of the LSI system, so that some form of higher-order analysis has to be employed. In this work, a general iterative solution based on a Bayesian approach is illustrated, and some cases both for mono and bidimensional applications are discussed. The method implies the use of non second-order statistics (rather than higher-order statistics), tuned to specific a priori statistical models. The Bayesian approach yields specific solutions corresponding to known techniques, such as MED deconvolution employed in seismic processing, and more sophisticated procedures for non-independent identically distributed (for instance Markovian) inputs. !12
机译:摘要:盲反卷积是解决未知LSI系统反转问题的典型解决方案。当只有输出可用时,二阶统计信息不足以检索LSI系统的相位,因此必须采用某种形式的高阶分析。在这项工作中,说明了基于贝叶斯方法的一般迭代解决方案,并讨论了针对单维和二维应用程序的一些情况。该方法意味着使用非二阶统计量(而不是高阶统计量),并调整到特定的先验统计模型。贝叶斯方法产生对应于已知技术的特定解决方案,例如地震处理中采用的MED反卷积,以及用于非独立的相同分布(例如马尔可夫式)输入的更复杂的过程。 !12

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