mlsanityData to 14 Oct 2026

Noise-resilient approach for deep tomographic imaging

arXiv 2211.15456 ↗First posted 22 Nov 2022eess.IV cs.CVcited by 1

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Abstract (arXiv)

We propose a noise-resilient deep reconstruction algorithm for X-ray tomography. Our approach shows strong noise resilience without obtaining noisy training examples. The advantages of our framework may further enable low-photon tomographic imaging.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 22 Nov 2022.

Builds on

4 of 13 references.

References · 4

  1. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 433
  2. 2019On instabilities of deep learning in image reconstruction - Does AI come at a cost?cited by 212
  3. 2020Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learningcited by 6
  4. 2022Physics-assisted Generative Adversarial Network for X-Ray Tomographycited by 3

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Invariant Scattering Transform for Medical Imagingcited by 0