Document Type
Article
Publication Date
3-1-2017
Publication Title
International Scholarly and Scientific Research & Innovation
Volume
11
Issue
4
First page number:
466
Last page number:
469
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
File Format
File Size
589 KB
Language
eng
Rights
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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),
466-469.
http://dx.doi.org/10.5281/zenodo.1130109