@inproceedings{ee04c612e3ee455ea001f0645f120835,
title = "Model-based image reconstruction with a hybrid regularizer",
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.",
author = "Jingyan Xu and Fr{\'e}d{\'e}ric Noo",
note = "Funding Information: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R21CA211035. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The work of F. Noo was also partly supported by Siemens Healthcare, GmbH. Publisher Copyright: {\textcopyright} 2018 SPIE.; Medical Imaging 2018: Physics of Medical Imaging ; Conference date: 12-02-2018 Through 15-02-2018",
year = "2018",
doi = "10.1117/12.2293781",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Schmidt, {Taly Gilat} and Guang-Hong Chen and Lo, {Joseph Y.}",
booktitle = "Medical Imaging 2018",
}