The Convergence of Two Algorithms for Compressed Sensing Based Tomography

Xiezhang  Li; Jiehua  Zhu

doi:10.4236/act.2012.13007

Xiezhang Li^*, Jiehua Zhu

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.

Keywords: Compressed Sensing; Image Reconstruction; Total Variation Minimization; Block Iterative Methods

Cite this paper: Li, X. and Zhu, J. (2012) The Convergence of Two Algorithms for Compressed Sensing Based Tomography. Advances in Computed Tomography, 1, 30-36. doi: 10.4236/act.2012.13007.

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