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A General Total Variation Minimization Theorem for Compressed Sensing Based Interior Tomography

机译:基于压缩传感的内部层析成像的总总变化最小化定理

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摘要

Recently, in the compressed sensing framework we found that a two-dimensional interior region-of-interest (ROI) can be exactly reconstructed via the total variation minimization if the ROI is piecewise constant (Yu and Wang, 2009). Here we present a general theorem charactering a minimization property for a piecewise constant function defined on a domain in any dimension. Our major mathematical tool to prove this result is functional analysis without involving the Dirac delta function, which was heuristically used by Yu and Wang (2009).
机译:最近,在压缩感测框架中,我们发现,如果ROI分段为常数,则可以通过将总变化最小化来精确地重建二维内部感兴趣区域(ROI)(Yu和Wang,2009)。在这里,我们提出了一个一般性定理,该定理描述了在任何维度上定义在域上的分段常数函数的最小化性质。我们证明这一结果的主要数学工具是不涉及Dirac delta函数的泛函分析,而Yurac和Wang(2009)启发式地使用了Dirac delta函数。

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