›› 2014, Vol. 23 ›› Issue (7): 78703-078703.doi: 10.1088/1674-1056/23/7/078703

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇    下一篇

Image reconstruction from few views by l0-norm optimization

孙玉立, 陶进绪   

  1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China
  • 收稿日期:2013-11-18 修回日期:2014-01-17 出版日期:2014-07-15 发布日期:2014-07-15

Image reconstruction from few views by l0-norm optimization

Sun Yu-Li (孙玉立), Tao Jin-Xu (陶进绪)   

  1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China
  • Received:2013-11-18 Revised:2014-01-17 Online:2014-07-15 Published:2014-07-15
  • Contact: Tao Jin-Xu E-mail:tjingx@ustc.edu.cn
  • About author:87.57.Q-; 87.55.kd

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摘要： 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.

关键词: iterative hard thresholding, few views reconstruction, sparse, l0-norm optimization

Abstract: 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.

Key words: iterative hard thresholding, few views reconstruction, sparse, l0-norm optimization

中图分类号:  (Computed tomography)

  • 87.57.Q-
87.55.kd (Algorithms)