Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization
Sparse-projection image reconstruction is a useful approach to lower the radiation dose; however, the incompleteness of projection data will cause degeneration of imaging quality. As a typical compressive sensing method, total variation has obtained great attention on this problem. Suffering from th...
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2014-01-01
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Series: | The Scientific World Journal |
Online Access: | http://dx.doi.org/10.1155/2014/458496 |
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author | Yi Zhang Weihua Zhang Jiliu Zhou |
author_facet | Yi Zhang Weihua Zhang Jiliu Zhou |
author_sort | Yi Zhang |
collection | DOAJ |
description | Sparse-projection image reconstruction is a useful approach to lower the radiation dose; however, the incompleteness of projection data will cause degeneration of imaging quality. As a typical compressive sensing method, total variation has obtained great attention on this problem. Suffering from the theoretical imperfection, total variation will produce blocky effect on smooth regions and blur edges. To overcome this problem, in this paper, we introduce the nonlocal total variation into sparse-projection image reconstruction and formulate the minimization problem with new nonlocal total variation norm. The qualitative and quantitative analyses of numerical as well as clinical results demonstrate the validity of the proposed method. Comparing to other existing methods, our method more efficiently suppresses artifacts caused by low-rank reconstruction and reserves structure information better. |
format | Article |
id | doaj-art-9d576322344a4fc38d4a4334da06cfe2 |
institution | Kabale University |
issn | 2356-6140 1537-744X |
language | English |
publishDate | 2014-01-01 |
publisher | Wiley |
record_format | Article |
series | The Scientific World Journal |
spelling | doaj-art-9d576322344a4fc38d4a4334da06cfe22025-02-03T06:12:53ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/458496458496Accurate Sparse-Projection Image Reconstruction via Nonlocal TV RegularizationYi Zhang0Weihua Zhang1Jiliu Zhou2College of Computer Science, Sichuan University, No. 24, South Section 1, Yihuan Road, Chengdu 610065, ChinaCollege of Computer Science, Sichuan University, No. 24, South Section 1, Yihuan Road, Chengdu 610065, ChinaCollege of Computer Science, Sichuan University, No. 24, South Section 1, Yihuan Road, Chengdu 610065, ChinaSparse-projection image reconstruction is a useful approach to lower the radiation dose; however, the incompleteness of projection data will cause degeneration of imaging quality. As a typical compressive sensing method, total variation has obtained great attention on this problem. Suffering from the theoretical imperfection, total variation will produce blocky effect on smooth regions and blur edges. To overcome this problem, in this paper, we introduce the nonlocal total variation into sparse-projection image reconstruction and formulate the minimization problem with new nonlocal total variation norm. The qualitative and quantitative analyses of numerical as well as clinical results demonstrate the validity of the proposed method. Comparing to other existing methods, our method more efficiently suppresses artifacts caused by low-rank reconstruction and reserves structure information better.http://dx.doi.org/10.1155/2014/458496 |
spellingShingle | Yi Zhang Weihua Zhang Jiliu Zhou Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization The Scientific World Journal |
title | Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization |
title_full | Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization |
title_fullStr | Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization |
title_full_unstemmed | Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization |
title_short | Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization |
title_sort | accurate sparse projection image reconstruction via nonlocal tv regularization |
url | http://dx.doi.org/10.1155/2014/458496 |
work_keys_str_mv | AT yizhang accuratesparseprojectionimagereconstructionvianonlocaltvregularization AT weihuazhang accuratesparseprojectionimagereconstructionvianonlocaltvregularization AT jiliuzhou accuratesparseprojectionimagereconstructionvianonlocaltvregularization |