An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection
The l1-norm regularization has attracted attention for image reconstruction in computed tomography. The l0-norm of the gradients of an image provides a measure of the sparsity of gradients of the image. In this paper, we present a new combined l1-norm and l0-norm regularization model for image recon...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2020/8873865 |
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| _version_ | 1850166901974499328 |
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| author | Xiezhang Li Guocan Feng Jiehua Zhu |
| author_facet | Xiezhang Li Guocan Feng Jiehua Zhu |
| author_sort | Xiezhang Li |
| collection | DOAJ |
| description | The l1-norm regularization has attracted attention for image reconstruction in computed tomography. The l0-norm of the gradients of an image provides a measure of the sparsity of gradients of the image. In this paper, we present a new combined l1-norm and l0-norm regularization model for image reconstruction from limited projection data in computed tomography. We also propose an algorithm in the algebraic framework to solve the optimization effectively using the nonmonotone alternating direction algorithm with hard thresholding method. Numerical experiments indicate that this new algorithm makes much improvement by involving l0-norm regularization. |
| format | Article |
| id | doaj-art-e4f704ce4bc943d89b9045ba619c28b8 |
| institution | OA Journals |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-e4f704ce4bc943d89b9045ba619c28b82025-08-20T02:21:19ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962020-01-01202010.1155/2020/88738658873865An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited ProjectionXiezhang Li0Guocan Feng1Jiehua Zhu2Department of Mathematical Sciences, Georgia Southern University, Statesboro 30460, USASchool of Mathematics, Sun Yat-sen University, Guangzhou 510275, ChinaDepartment of Mathematical Sciences, Georgia Southern University, Statesboro 30460, USAThe l1-norm regularization has attracted attention for image reconstruction in computed tomography. The l0-norm of the gradients of an image provides a measure of the sparsity of gradients of the image. In this paper, we present a new combined l1-norm and l0-norm regularization model for image reconstruction from limited projection data in computed tomography. We also propose an algorithm in the algebraic framework to solve the optimization effectively using the nonmonotone alternating direction algorithm with hard thresholding method. Numerical experiments indicate that this new algorithm makes much improvement by involving l0-norm regularization.http://dx.doi.org/10.1155/2020/8873865 |
| spellingShingle | Xiezhang Li Guocan Feng Jiehua Zhu An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection International Journal of Biomedical Imaging |
| title | An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection |
| title_full | An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection |
| title_fullStr | An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection |
| title_full_unstemmed | An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection |
| title_short | An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection |
| title_sort | algorithm of l1 norm and l0 norm regularization algorithm for ct image reconstruction from limited projection |
| url | http://dx.doi.org/10.1155/2020/8873865 |
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