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Robustness of data-driven approaches in limited angle tomography

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arxiv 2403.11350 v3 pith:VMK5SYEN submitted 2024-03-17 math.NA cs.LGcs.NA

Yiran Wang , Yimin Zhong This is my paper · ORCID
classification math.NAcs.LGcs.NA
keywords angleapproachesdata-drivenlimitedadditionbackprojectioncompareddifficult
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The limited angle Radon transform is notoriously difficult to invert due to its ill-posedness. In this work, we give a mathematical explanation that data-driven approaches can stably reconstruct more information compared to traditional methods like filtered backprojection. In addition, we use experiments based on the U-Net neural network to validate our theory.

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Cited by 1 Pith paper

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  1. Revisiting $\Psi$DONet: microlocally inspired filters for incomplete-data tomographic reconstructions

    math.OC 2025-01 conditional novelty 5.0 of 10

    Microlocally inspired mask-shaped filters let ΨDONet match or slightly improve limited- and sparse-angle CT reconstructions with far fewer learnable parameters.

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