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An information-theoretic approach to analyzing CLEAN

机译:信息理论方法分析CLEAN

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摘要

We analyze the deconvolution technique, CLEAN, from an information-theoretic perspective. The CLEAN algorithm is an iterative technique, which subtracts out the target mass from the dirty image at each stage. However, each iterative step also alters the information content in the image. We investigate how the information content, measured in terms of the differential entropy, varies during the successive steps. Closed-form expressions have been derived and simulations have been carried out to corroborate the theory. It is shown that the image entropy is useful as an additional input for determining the stopping criterion for CLEAN.
机译:我们从信息理论的角度分析了反卷积技术CLEAN。 CLEAN算法是一种迭代技术,它在每个阶段都从脏图像中减去目标质量。但是,每个迭代步骤也会更改图像中的信息内容。我们研究了以微分熵衡量的信息含量在后续步骤中如何变化。得出了封闭形式的表达式,并进行了模拟以证实该理论。结果表明,图像熵可用作确定CLEAN停止标准的附加输入。

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