A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography
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...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2019/8398035 |
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| author | Jiehua Zhu Xiezhang Li |
| author_facet | Jiehua Zhu Xiezhang Li |
| author_sort | Jiehua Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-86f8dccc66214bacaa330915a6246527 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-86f8dccc66214bacaa330915a62465272025-02-03T06:12:48ZengWileyJournal of Applied Mathematics1110-757X1687-00422019-01-01201910.1155/2019/83980358398035A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed TomographyJiehua Zhu0Xiezhang Li1Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA 30460, USADepartment of Mathematical Sciences, Georgia Southern University, Statesboro, GA 30460, USAThe 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.http://dx.doi.org/10.1155/2019/8398035 |
| spellingShingle | Jiehua Zhu Xiezhang Li A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography Journal of Applied Mathematics |
| title | A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography |
| title_full | A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography |
| title_fullStr | A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography |
| title_full_unstemmed | A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography |
| title_short | A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography |
| title_sort | smoothed l0 norm and l1 norm regularization algorithm for computed tomography |
| url | http://dx.doi.org/10.1155/2019/8398035 |
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