Image reconstruction from few views by l0-norm optimization

In the medical computer tomography （CT） field, total variation （TV）, which is the l1-norm of the discrete gradient transform （DGT）, is widely used as regularization based on the compressive sensing （CS） theory. To overcome the TV model＇s disadvantageous tendency of uniformly penalizing the image gra...

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Bibliographic Details
Published in:中国物理B：英文版 2014-07 (7), p.762-766
Main Author: Sun Yu-Li Tao Jin-Xu
Format: Article
Language:English
Subjects:
DGT
L1范数
优化
低对比度
图像梯度
图像重建
计算机断层扫描
迭代算法
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Summary:In the medical computer tomography （CT） field, total variation （TV）, which is the l1-norm of the discrete gradient transform （DGT）, is widely used as regularization based on the compressive sensing （CS） theory. To overcome the TV model＇s disadvantageous tendency of uniformly penalizing the image gradient and over smoothing the low-contrast structures, an iterative algorithm based on the l0-norm optimization of the DGT is proposed. In order to rise to the challenges introduced by the l0-norm DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an iterative hard thresholding （IHT） algorithm, whose convergence and effective efficiency have been theoretically proven. The simulation demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.
ISSN:1674-1056
2058-3834
DOI:10.1088/1674-1056/23/7/078703