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Article
The Convergence of Block Cyclic Projection with Underrelaxation Parameters for Compressed Sensing Based Tomography
Journal of X-ray Science and Technology
  • Fangjun Arroyo, Francis Marion University
  • Edward Arroyo, American Public University System
  • Xiezhang Li, Georgia Southern University
  • Jiehua Zhu, Georgia Southern University
Document Type
Article
Publication Date
1-1-2014
DOI
10.3233/XST-140419
Disciplines
Abstract
The block cyclic projection method in the compressed sensing framework (BCPCS) was introduced for image reconstruction in computed tomography and its convergence had been proven in the case of unity relaxation (λ=1). In this paper, we prove its convergence with underrelaxation parameters λ∈(0,1). As a result, the convergence of compressed sensing based block component averaging algorithm (BCAVCS) and block diagonally-relaxed orthogonal projection algorithm (BDROPCS) with underrelaxation parameters under a certain condition are derived. Experiments are given to illustrate the convergence behavior of these algorithms with selected parameters.
Citation Information
Fangjun Arroyo, Edward Arroyo, Xiezhang Li and Jiehua Zhu. "The Convergence of Block Cyclic Projection with Underrelaxation Parameters for Compressed Sensing Based Tomography" Journal of X-ray Science and Technology Vol. 22 Iss. 2 (2014) p. 197 - 211
Available at: http://works.bepress.com/jiehua_zhu/7/