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Convergence properties of hybrid blind deconvolution

机译:杂交盲折叠的收敛性能

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The multi-frame blind deconvolution algorithm is considered for processing astronomical speckle images when only a few frames of data are collected. It has been noted that when the speckle data contains even moderate amounts of shot noise the algorithm often converges to the trivial point solution. The convergence properties of the Knox-Thompson algorithm are well understood, however when only a few frames of data are used, the algorithm often contains false structure referred to as processing artifacts. In this paper we consider penalized blind convolution as a `clean up' for the Knox-Thompson algorithm. The utility of this combination is demonstrated images of the Jovian moon Ganymede taken on a 1.5 meter telescope.
机译:当仅收集几帧数据时,考虑了多帧盲折叠算法用于处理天文斑点图像。已经注意到,当散斑数据包含甚至适量的镜头噪声时,算法通常会收敛到琐碎的点解决方案。 knox-Thompson算法的收敛性质很好地理解,但是当使用几个数据帧时,算法通常包含称为处理伪影的错误结构。在本文中,我们认为诺克洛汤普森算法的“清理”是惩罚的盲目卷积。这种组合的效用是在1.5米望远镜上采取的Jovian Moon Ganymede的图像。

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