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The Convergence of Two Algorithms for Compressed Sensing Based Tomography

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DOI: 10.4236/act.2012.13007    2,596 Downloads   5,452 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

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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