Convergence of block cyclic projection and Cimmino algorithms for compressed sensing based tomography

被引:4
|
作者
Li, Xiezhang [1 ]
Zhu, Jiehua [1 ]
机构
[1] Georgia So Univ, Dept Math Sci, Statesboro, GA 30460 USA
关键词
Computerized tomography (CT); compressed sensing; total variation; amalgamated projection methods; block cyclic projection method; block Cimmino's algorithm; DISCRETE TOMOGRAPHY; RECONSTRUCTION; MODEL;
D O I
10.3233/XST-2010-0267
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
The amalgamated projection method for convex feasibility and optimization problems has recently been proposed and the stable convergence under summable perturbations has been derived. As an application in computerized tomography (CT), the accuracy and the rate of convergence of the cyclic projection method and Cimmino algorithm incorporated with total variation minimization under certain conditions are significantly improved based on the theory of compressed sensing. In this paper, a varying block cyclic projection method and a block Cimmino's algorithm in the compressed sensing framework are proposed and their convergence are derived with an application of the convergence theorem of the amalgamated projection methods. An example is given to illustrate the convergence behavior of new algorithms.
引用
收藏
页码:369 / 379
页数:11
相关论文
共 50 条
  • [31] Compressed sensing algorithms for fan-beam computed tomography image reconstruction
    Zhang, Jun
    Wang, Jun
    Zuo, Hongquan
    Xu, Guangwu
    Thibault, Jean-Baptiste
    OPTICAL ENGINEERING, 2012, 51 (07)
  • [32] A convergence analysis of hybrid gradient projection algorithm for constrained nonlinear equations with applications in compressed sensing
    Li, Dandan
    Wang, Songhua
    Li, Yong
    Wu, Jiaqi
    NUMERICAL ALGORITHMS, 2024, 95 (03) : 1325 - 1345
  • [33] A projection-based sparse-view virtual monochromatic computed tomography method based on a compressed-sensing algorithm
    Park, J.
    Kim, G.
    Lim, Y.
    Cho, H.
    Park, C.
    Kim, K.
    Kang, S.
    Lee, D.
    Park, S.
    Lim, H.
    Lee, H.
    Jeon, D.
    Kim, W.
    Seo, C.
    Lee, E.
    JOURNAL OF INSTRUMENTATION, 2019, 14
  • [34] A convergence analysis of hybrid gradient projection algorithm for constrained nonlinear equations with applications in compressed sensing
    Dandan Li
    Songhua Wang
    Yong Li
    Jiaqi Wu
    Numerical Algorithms, 2024, 95 : 1325 - 1345
  • [35] The finite steps of convergence of the fast thresholding algorithms with f-feedbacks in compressed sensing
    Ningning Han
    Jian Lu
    Shidong Li
    Numerical Algorithms, 2022, 90 : 1197 - 1223
  • [36] The finite steps of convergence of the fast thresholding algorithms with f-feedbacks in compressed sensing
    Han, Ningning
    Lu, Jian
    Li, Shidong
    NUMERICAL ALGORITHMS, 2022, 90 (03) : 1197 - 1223
  • [37] Block Compressed Sensing Observation Matrix Optimization Algorithm Based on Block Target
    Xu, Shi-Fu
    Jiang, Ya-Nan
    Journal of Computers (Taiwan), 2021, 32 (06): : 218 - 226
  • [38] Remote-sensing Fusion by Multiscale Block-based Compressed Sensing
    Yang Senlin
    Chong Xin
    PROCEEDINGS OF THE 2015 4TH NATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING ( NCEECE 2015), 2016, 47 : 1557 - 1560
  • [39] Deep denoiser prior and smoothed projection landweber inspired block-wise compressed sensing
    Zong, Chun-mei
    Zhang, Yue-qin
    Zhao, Qing-Shan
    INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS, 2022, 15 (04) : 347 - 358
  • [40] Compressed sensing MRI based on the hybrid regularization by denoising and the epigraph projection
    Lian Qiusheng
    Fan Xiaoyu
    Shi Baoshun
    Zhang Xiaohua
    SIGNAL PROCESSING, 2020, 170