The Convergence of Two Algorithms for Compressed Sensing Based Tomography

Authors

Xiezhang Li, Georgia Southern UniversityFollow
Jiehua Zhu, Georgia Southern UniversityFollow

Document Type

Article

Publication Date

12-20-2012

Publication Title

Advanced in Computed Tomography

DOI

10.4236/act.2012.13007

ISSN

2169-2483

Abstract

The constrained total variation minimization has been developed successfully for image reconstruction in computed tomography. In this paper, the block component averaging and diagonally-relaxed orthogonal projection methods are proposed to incorporate with the total variation minimization in the compressed sensing framework. The convergence of the algorithms under a certain condition is derived. Examples are given to illustrate their convergence behavior and noise performance.

Comments

This article is published under the Creative Commons license (CC BY or CC BY-NC). Article obtained from the Advances in Computed Tomography.

Recommended Citation

Li, Xiezhang, Jiehua Zhu. 2012. "The Convergence of Two Algorithms for Compressed Sensing Based Tomography." Advanced in Computed Tomography, 1 (3): 30-36. doi: 10.4236/act.2012.13007
https://digitalcommons.georgiasouthern.edu/math-sci-facpubs/87