Robustness of data-driven approaches in limited angle tomography
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 17 Mar 2024.
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References · 12
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
- 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 433
- 2019On the Inductive Bias of Neural Tangent Kernelscited by 177
- 2018Universal approximations of invariant maps by neural networkscited by 127
- 2020Solving Inverse Problems With Deep Neural Networks -- Robustness Included?cited by 58
- 2018Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomographycited by 29
- 2020Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomographycited by 15
- 2018High-Resolution Limited-Angle Phase Tomography of Dense Layered Objects Using Deep Neural Networkscited by 9
- 2023Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Datacited by 5
- 2017Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstructioncited by 3
- 2021Learning a microlocal prior for limited-angle tomographycited by 1
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