The convergence of block cyclic projection with underrelaxation parameters for compressed sensing based tomography

被引：2
|
作者：
Arroyo, Fangjun [1 ]
Arroyo, Edward [2 ]
Li, Xiezhang [3 ]
Zhu, Jiehua [3 ]
机构：
[1] Francis Marion Univ, Dept Math, Florence, SC 29501 USA
[2] Amer Publ Univ Syst, Sch Sci Technol, Manassas, VA USA
[3] Georgia So Univ, Dept Math Sci, Statesboro, GA 30460 USA
来源：
JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY | 2014年 / 22卷 / 02期
关键词：
Compressed sensing; image reconstruction; total variation minimization; amalgamated projection method; block iterative algorithm; ITERATIVE ALGORITHMS; IMAGE-RECONSTRUCTION; DISCRETE TOMOGRAPHY; MODEL;
D O I：
10.3233/XST-140419
中图分类号：
TH7 [仪器、仪表];
学科分类号：
0804 ; 080401 ; 081102 ;
摘要：
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 (lambda = 1). In this paper, we prove its convergence with underrelaxation parameters lambda is an element of (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.
引用
收藏
页码：197 / 211
页数：15
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