Laser & Optoelectronics Progress, Volume. 56, Issue 21, 211101(2019)

Application of Total Variation Minimization Algorithm Based on Beetle Antennae Search on Computed Tomography Interior Reconstruction

Huihua Kong1,2、*, Yingbo Sun1,2、**, and Yanxia Zhang1,2
Author Affiliations
  • 1School of Science, North University of China, Taiyuan, Shanxi 0 30051, China
  • 2Shanxi Key Laboratory of Information Detection and Processing, North University of China, Taiyuan, Shanxi 0 30051, China
  • show less

    Region of interest is sliced smooth or polynomial, then accurate internal reconstruction can be performed by total variation (TV) minimization. The solution of TV minimization usually adopts the gradient descent method, taking the negative gradient of the objective function as the search direction, and then optimizes iteratively the objective function. In order to improve the efficiency of TV minimizing, this paper proposes a method to find the optimal solution direction by combining beetle antennae search (BAS) and gradient descent. The method selects the gradient descent direction or the optimal solution direction which is based on the individual “left and right whiskers” to iterate, according to the generated random number and threshold during the TV minimization process. The simulation experiment and the actual experiment show that the proposed algorithm has fast convergence speed and good reconstruction effect.

    Tools

    Get Citation

    Copy Citation Text

    Huihua Kong, Yingbo Sun, Yanxia Zhang. Application of Total Variation Minimization Algorithm Based on Beetle Antennae Search on Computed Tomography Interior Reconstruction[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211101

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Imaging Systems

    Received: Mar. 25, 2019

    Accepted: Apr. 30, 2019

    Published Online: Nov. 2, 2019

    The Author Email: Kong Huihua (huihuak@163.com), Sun Yingbo (1379058385@qq.com)

    DOI:10.3788/LOP56.211101

    Topics