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Author:

Zhang, Zhen (Zhang, Zhen.) | Shi, Yunhui (Shi, Yunhui.) (Scholars:施云惠) | Yin, Baocai (Yin, Baocai.) (Scholars:尹宝才)

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EI Scopus

Abstract:

Since Magnetic resonance(MR) Images commonly possess a blocky structure and have sparse representations under certain wavelet bases, total variation (TV) and wavelet domain 1 norm regularization are often enforced together (TV-wavelet method) to improve the recovery accuracy. However, this model ignores that a family of wavelet coefficients has a natural grouping of its components. In this paper, we propose a new TV-Group sparse model which combines TV and wavelet domain group sparse penalty. The corresponding algorithm based on composite splitting method is employed to approach this TV-Group sparse model. Experimental results show that our model can obviously improve both objective and subjective qualities of MR image recovery comparing with the TV-wavelet model. © 2013 IEEE.

Keyword:

Image enhancement Magnetic resonance Image reconstruction Magnetic resonance imaging

Author Community:

  • [ 1 ] [Zhang, Zhen]College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Shi, Yunhui]College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Yin, Baocai]College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, China

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ISSN: 1945-7871

Year: 2013

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 4

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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