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
  • [21] BLOCK-BASED ADAPTIVE COMPRESSED SENSING FOR VIDEO
    Liu, Zhaorui
    Zhao, H. Vicky
    Elezzabi, A. Y.
    2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, 2010, : 1649 - 1652
  • [22] Imaging of Transmission Equipment based on Block Compressed Sensing
    Zhao, Jingjing
    Sun, Jixiang
    Zhou, Shilin
    Hu, Lei
    DIGITAL MANUFACTURING & AUTOMATION III, PTS 1 AND 2, 2012, 190-191 : 998 - 1001
  • [23] Clustering compressed sensing based on image block similarities
    Li, Wei-Wei
    Jiang, Ting
    Wang, Ning
    Journal of China Universities of Posts and Telecommunications, 2014, 21 (04): : 68 - 76
  • [24] Block-Based Compressed Sensing of Images and Video
    Fowler, James E.
    Mun, Sungkwang
    Tramel, Eric W.
    FOUNDATIONS AND TRENDS IN SIGNAL PROCESSING, 2010, 4 (04): : 297 - 416
  • [25] Image reconstruction based on improved block compressed sensing
    Du, Hong
    Lin, Huixian
    COMPUTATIONAL & APPLIED MATHEMATICS, 2022, 41 (01):
  • [26] SAR IMAGES COMPRESSED SENSING BASED ON RECOVERY ALGORITHMS
    Rouabah, Slim
    Ouarzeddine, Mounira
    Souissi, Boularbah
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 8897 - 8900
  • [27] Clustering compressed sensing based on image block similarities
    LI Wei-wei
    JIANG Ting
    WANG Ning
    The Journal of China Universities of Posts and Telecommunications, 2014, 21 (04) : 68 - 76
  • [28] Route selection algorithms utilizing the property of the ZDD for compressed sensing-based transmissive network tomography
    Naka, Teruhito
    Hara, Shinsuke
    8TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT-2017) AND THE 7TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT 2017), 2017, 109 : 124 - 131
  • [29] Supplemental analysis on compressed sensing based interior tomography
    Yu, Hengyong
    Yang, Jiansheng
    Jiang, Ming
    Wang, Ge
    PHYSICS IN MEDICINE AND BIOLOGY, 2009, 54 (18): : N425 - N432
  • [30] Tomography SAR Imaging Based on Distributed Compressed Sensing
    Ren, Xiaozhen
    Qin, Yao
    Qiao, Lihong
    Li, Pengpeng
    2016 PROGRESS IN ELECTROMAGNETICS RESEARCH SYMPOSIUM (PIERS), 2016, : 3588 - 3591