Model-based image reconstruction with a hybrid regularizer

Jingyan Xu, Frédéric Noo

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

Abstract

Model based image reconstruction often includes regularizers to encourage a priori image information and stabilize the ill-posed inverse problem. Popular edge preserving regularizers often penalize the first order differences of image intensity values. In this work, we propose a hybrid regularizer that additionally penalizes the gradient of an auxiliary variable embedded in the half-quadratic reformulation of some popular edge preserving functions. As the auxiliary variable contain the gradient information, the hybrid regularizer penalizes both the first order and the second order image intensity differences, hence encourages both piecewise constant and piecewise linear image intensity values. Our experimental data using combined physical data acquisition and computer simulations demonstrate the effectiveness of the hybrid regularizer in reducing the stair-casing artifact of the TV penalty, and producing smooth intensity variations.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationPhysics of Medical Imaging
PublisherSPIE
Volume10573
ISBN (Electronic)9781510616356
DOIs
StatePublished - Jan 1 2018
EventMedical Imaging 2018: Physics of Medical Imaging - Houston, United States
Duration: Feb 12 2018Feb 15 2018

Other

OtherMedical Imaging 2018: Physics of Medical Imaging
CountryUnited States
CityHouston
Period2/12/182/15/18

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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