TRex: A Tomography Reconstruction Proximal Framework for Robust Sparse View X-Ray Applications: A Tomography Reconstruction Proximal Framework for Robust Sparse View X-Ray Applications

Mohamed Aly, Guangming Zang, Wolfgang Heidrich, Peter Wonka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present TRex, a flexible and robust Tomographic Reconstruction framework using proximal algorithms. We provide an overview and perform an experimental comparison between the famous iterative reconstruction methods in terms of reconstruction quality in sparse view situations. We then derive the proximal operators for the four best methods. We show the flexibility of our framework by deriving solvers for two noise models: Gaussian and Poisson; and by plugging in three powerful regularizers. We compare our framework to state of the art methods, and show superior quality on both synthetic and real datasets.
Original languageEnglish (US)
Title of host publicationA Tomography Reconstruction Proximal Framework for Robust Sparse View X-Ray Applications
StatePublished - Jun 11 2016

Keywords

  • math.OC
  • cs.CV
  • cs.LG
  • stat.ML

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