东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2011, Vol. 32 ›› Issue (4): 460-463.DOI: -

• 论著 • 上一篇    下一篇

基于Tikhonov和变差正则化的磁感应断层成像重建算法

陈玉艳;王旭;吕轶;杨丹;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(50477015)

An image reconstruction algorithm based on Tikhonov and variation regularization for magnetic induction tomography

Chen, Yu-Yan (1); Wang, Xu (1); Lü, Yi (1); Yang, Dan (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Chen, Y.-Y.
  • About author:-
  • Supported by:
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摘要： 为了解决磁感应断层成像(MIT)逆问题的病态性和改善重建图像的质量,提出一种新的组合算法.该组合算法首先利用Tikhonov正则化算法对解的适定性产生初步的成像区域,之后再利用变差正则化算法对解的保边缘性和锐化作用进行图像重建.该组合算法与Tikhonov正则化算法及变差正则化算法相比,不仅有效地克服了磁感应断层成像(MIT)重建图像数值解的不稳定性,还加快了重建图像的收敛速度,提高了目标导体的分辨能力,有效改善了重构图像的质量.仿真结果表明了该组合算法的有效性.

关键词: 磁感应断层成像(MIT), 重建图像, Tikhonov正则化, 变差正则化, 组合算法

Abstract: A new hybrid algorithm is presented to solve the ill-conditioned inverse problem of magnetic induction tomography (MIT) and improve the quality of reconstructed images. The hybrid algorithm first produces the preliminary image region using the solution well-posedness of the Tikhonov regularization algorithm, and then obtains the final reconstructed image using the edge-preserving and sharpening property of the variation regularization algorithm. Compared with the Tikhonov regularization algorithm and the variation regularization algorithm, the hybrid algorithm overcame the numerical instability of MIT image reconstruction, accelerated the convergence speed of image reconstruction, and improved the resolving power of target conductors and the quality of the reconstructed image. Simulation results verified the effectiveness of the proposed algorithm.

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