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arXiv · 2103.05122

Low-Rank Tensor Regression for X-Ray Tomography

Sanket R. Jantre·Zichao Wendy Di
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Abstract

Tomographic imaging is useful for revealing the internal structure of a 3D sample. Classical reconstruction methods treat the object of interest as a vector to estimate its value. Such an approach, however, can be inefficient in analyzing high-dimensional data because of the underexploration of the underlying structure. In this work, we propose to apply a tensor-based regression model to perform tomographic reconstruction. Furthermore, we explore the low-rank structure embedded in the corresponding tensor form. As a result, our proposed method efficiently reduces the dimensionality of the unknown parameters, which is particularly beneficial for ill-posed inverse problem suffering from insufficient data. We demonstrate the robustness of our proposed approach on synthetic noise-free data as well as on Gaussian noise-added data.

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Sanket R. Jantre, Zichao Wendy Di. 2021-03-08. Low-Rank Tensor Regression for X-Ray Tomography. https://doi.org/10.1109/icip42928.2021.9506199

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