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Proceedings Paper

A novel scatter-matrix eigenvalues-based total variation (SMETV) regularization for medical image restoration
Author(s): Zhenghua Huang; Tianxu Zhang; Lihua Deng; Hao Fang; Qian Li
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Paper Abstract

Total variation(TV) based on regularization has been proven as a popular and effective model for image restoration, because of its ability of edge preserved. However, as the TV favors a piece-wise constant solution, the processing results in the flat regions of the image are easily produced "staircase effects", and the amplitude of the edges will be underestimated; the underlying cause of the problem is that the regularization parameter can not be changeable with spatial local information of image. In this paper, we propose a novel Scatter-matrix eigenvalues-based TV(SMETV) regularization with image blind restoration algorithm for deblurring medical images. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish edges from flat areas. The proposed algorithm can effectively reduce the noise in flat regions as well as preserve the edge and detailed information. Moreover, it becomes more robust with the change of the regularization parameter. Extensive experiments demonstrate that the proposed approach produces results superior to most methods in both visual image quality and quantitative measures.

Paper Details

Date Published: 14 December 2015
PDF: 6 pages
Proc. SPIE 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing, 98140D (14 December 2015); doi: 10.1117/12.2204820
Show Author Affiliations
Zhenghua Huang, Huazhong Univ. of Science and Technology (China)
Tianxu Zhang, Huazhong Univ. of Science and Technology (China)
Lihua Deng, Huazhong Univ. of Science and Technology (China)
Hao Fang, Wuhan Donghu Univ. (China)
Qian Li, South-Central Univ. for Nationalities (China)


Published in SPIE Proceedings Vol. 9814:
MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing
Jianguo Liu, Editor(s)

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