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Jan Sijbers

University of Antwerp

10 papers hereh-index 7122.7k citations579 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5
  • last author4

Across the 9 of 10 papers where every author was matched, so the position is known.

fields
  • eess.IV5
  • cond-mat.other1
  • math.NA1
  • math.OC1
  • physics.ed-ph1
  • physics.med-ph1
affiliations
  • University of Antwerp
same name
  • Jan Sijbers — 1 paper
  • Jan Sijbers — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20062023
most citedVirtual grating approach for Monte Carlo simulations of edge illumination-based x-ray phase contrast imaging

8 citations · 20 across the 9 of their papers we have counts for

collaborators
Showing 2019Show all

3 papers · 1 filter

eess.IV2019

To Recurse or not to Recurse,a Low Dose CT Study

Shabab Bazrafkan, Vincent Van Nieuwenhove, Jan Sijbers

Restoring high-quality CT images from low dose CT counterparts is an ill-posed, nonlinear problem to which Deep Learning approaches have been giving superior solutions compared to…

eess.IV2019★ 3 cited

Deep Neural Network Assisted Iterative Reconstruction Method for Low Dose CT

Shabab Bazrafkan, Vincent Van Nieuwenhove, Joris Soons +2

Low Dose Computed Tomography suffers from a high amount of noise and/or undersampling artefacts in the reconstructed image. In the current article, a Deep Learning technique is exp…

eess.IV2019★ 8 cited

Deep Learning Based Computed Tomography Whys and Wherefores

Shabab Bazrafkan, Vincent Van Nieuwenhove, Joris Soons +2

This is an article about the Computed Tomography (CT) and how Deep Learning influences CT reconstruction pipeline, especially in low dose scenarios.

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.