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A variational method using fractional order Hilbert spaces for tomographic reconstruction of blurred and noised binary images

机译:使用分数阶希尔伯特空间的变分方法,用于模糊和噪声二值图像的层析成像重建

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摘要

We provide in this article a refined functional analysis of the Radon operator restricted to axisymmetric functions, and show that it enjoys strong regularity properties in fractional order Hilbert spaces. This study is motivated by a problem of tomographic reconstruction of binary axially symmetric objects, for which we have available one single blurred and noised snapshot. We propose a variational approach to handle this problem, consisting in solving a minimization problem settled in adapted fractional order Hilbert spaces. We show the existence of solutions, and then derive first order necessary conditions for optimality in the form of optimality systems.
机译:我们在本文中提供了对Radon算子的精确泛函分析,该函数只限于轴对称函数,并表明它在分数阶Hilbert空间中具有很强的正则性。这项研究是基于对二进制轴对称物体进行层析成像重建的问题而提出的,为此,我们获得了一个模糊和噪杂的快照。我们提出了一种变分方法来处理此问题,包括解决在适应的分数阶Hilbert空间中解决的最小化问题。我们展示了解的存在,然后以最优系统的形式导出了最优的一阶必要条件。

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