A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-02
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
The nonmonotone alternating direction algorithm (NADA) was recently proposed for effectively solving a class of equality-constrained nonsmooth optimization problems and applied to the total variation minimization in image reconstruction, but the reconstructed images suffer from the artifacts.
Though by the l0-norm regularization the edge can be effectively retained, the problem is NP hard.
The smoothed l0-norm approximates the l0-norm as a limit of smooth convex functions and provides a smooth measure of sparsity in applications.
The smoothed l0-norm regularization has been an attractive research topic in sparse image and signal recovery.
In this paper, we present a combined smoothed l0-norm and l1-norm regularization algorithm using the NADA for image reconstruction in computed tomography.
We resolve the computation challenge resulting from the smoothed l0-norm minimization.
The numerical experiments demonstrate that the proposed algorithm improves the quality of the reconstructed images with the same cost of CPU time and reduces the computation time significantly while maintaining the same image quality compared with the l1-norm regularization in absence of the smoothed l0-norm.
American Psychological Association (APA)
Zhu, Jiehua& Li, Xiezhang. 2019. A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography. Journal of Applied Mathematics،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1168934
Modern Language Association (MLA)
Zhu, Jiehua& Li, Xiezhang. A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography. Journal of Applied Mathematics No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1168934
American Medical Association (AMA)
Zhu, Jiehua& Li, Xiezhang. A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography. Journal of Applied Mathematics. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1168934
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1168934