Sparse-View CT Reconstruction Based on Nonconvex L1 − L2 Regularizations

Authors

Ali Pour YazdanpanahFollow
Farideh Shahraki
Emma Regentova, University of Nevada, Las VegasFollow

Document Type

Article

Publication Date

4-1-2017

Publication Title

International Scholarly and Scientific Research & Innovation

Volume

11

Issue

4

First page number:

472

Last page number:

475

Abstract

We present a nonconvex, Lipschitz continuous and non-smooth regularization model. The CT reconstruction is formulated as a nonconvex constrained L1 − L2 minimization problem and solved through a difference of convex algorithm and alternating direction of multiplier method which generates a better result than L0 or L1 regularizers in the CT reconstruction.

Keywords

Computed tomography, sparse-view reconstruction, L1 −L2 minimization, non-convex, difference of convex functions

Language

eng

Repository Citation

Yazdanpanah, A. P., Shahraki, F., Regentova, E. (2017). Sparse-View CT Reconstruction Based on Nonconvex L1 − L2 Regularizations. International Scholarly and Scientific Research & Innovation, 11(4), 472-475.
http://dx.doi.org/10.5281/zenodo.1130109