Journal of Applied Mathematics

A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography

Jiehua Zhu and Xiezhang Li

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Abstract

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.

Article information

Source
J. Appl. Math., Volume 2019 (2019), Article ID 8398035, 8 pages.

Dates
Received: 5 November 2018
Revised: 5 April 2019
Accepted: 13 May 2019
First available in Project Euclid: 24 July 2019

Permanent link to this document
https://projecteuclid.org/euclid.jam/1563933640

Digital Object Identifier
doi:10.1155/2019/8398035

Mathematical Reviews number (MathSciNet)
MR3963598

Citation

Zhu, Jiehua; Li, Xiezhang. A Smoothed ${l}_{0}$ -Norm and ${l}_{1}$ -Norm Regularization Algorithm for Computed Tomography. J. Appl. Math. 2019 (2019), Article ID 8398035, 8 pages. doi:10.1155/2019/8398035. https://projecteuclid.org/euclid.jam/1563933640


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