Image reconstruction from few views by l0-norm optimization

doi:10.1088/1674-1056/23/7/078703

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.

Keywords: iterative hard thresholding few views reconstruction sparse l₀-norm optimization

Received: 18 November 2013
Revised: 17 January 2014
Accepted manuscript online:

PACS:	87.57.Q-	(Computed tomography)
	87.55.kd	(Algorithms)

Corresponding Authors: Tao Jin-Xu
E-mail: tjingx@ustc.edu.cn

About author: 87.57.Q-; 87.55.kd

Cite this article:

Sun Yu-Li (孙玉立), Tao Jin-Xu (陶进绪) Image reconstruction from few views by l₀-norm optimization 2014 Chin. Phys. B 23 078703

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Image reconstruction from few views by l₀-norm optimization