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Filtering-Free Blind Separation of Correlated Images

机译:无相关图像的无过滤盲分离

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When using ICA for image separation, a well-known problem is that most often a large correlation exists between the sources. Because of this dependence, there is no more guarantee that the global maximum of the ICA contrast matches the outputs to the sources. In order to overcome this problem, some preprocessing can be used, like e.g. band-pass filtering. However, those processings involve parameters, for which the optimal values could be tedious to adjust. In this paper, it is shown that a simple ICA algorithm can recover the sources, without any other preprocessing than whitening, when they are correlated in a specific way. First, a single source is extracted, and next, a parameter-free postprocessing is applied for optimizing the extraction of the remaining sources.
机译:当使用ICA进行图像分离时,众所周知的问题是,源之间的大多数情况往往存在大的相关性。由于这种依赖性,不再保证ICA对比度的全局最大值与源的输出匹配。为了克服这个问题,可以使用一些预处理,如例如,带通滤波。然而,这些处理涉及参数,最佳值可能是繁琐的调整。在本文中,示出了简单的ICA算法可以恢复源,而没有比美白的任何其他预处理,当它们以特定方式相关时。首先,提取单个源,然后,应用无参数后处理用于优化剩余源的提取。

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