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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Main Authors: Yi Zhang, Weihua Zhang, Jiliu Zhou
Format: Article
Language:English
Published: Wiley 2014-01-01
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