基于自适应步长双参数正则化算法的超声波过程层析成像图像重建

doi:10.3969/j.issn.1000-1158.2015.01.11

摘要
图/表
参考文献(11)
相关文章 (15)

全文: PDF (4197 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要提出了一种新的自适应步长双参数正则化算法,对超声波层析成像系统检测浆体浓度分布进行图像重建。该算法利用转换矩阵将超定解作为先验信息,嵌入到正则化泛函中,避免重建图像被过度平滑,不仅成像速度较快且重建图像具有较高分辨率。仿真实验结果表明,相比于Tikhonov正则化算法以及Landweber算法,自适应步长双参数正则化算法重建图像的相关系数有明显提高并且边界信息更加可靠。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	张琳
	邵富群
	周明

关键词 ：计量学, 超声波过程, 自适应步长, 双参数正则化算法, 先验信息, 层析成像, 图像重建

Abstract：A new adapted step two-parameter regularization algorithm is presented to reconstruct image in ultrasound tomography system for detecting distribution of slurry concentration. The overdetermined solution, as a priori information, is embedded into the regularization function by using the transition matrix for accelerate reconstruction.Higher space resolution is achieved and the over-smoothing deficiency of the reconstruction can be avoided effectively. The simulation results show that,compared to Tikhonov regularization algorithm and Landweber algorithm, the correlation coefficient of the reconstructed image by using SATPR are significantly improved and the boundary information of image is more reliable.

Key words： Metrology Ultrasound progress Adapted step Two-parameter regularization algorithm Priori information Tomography Image reconstruction

作者简介: 张琳（1980-）,男（满族）,辽宁沈阳人,博士,东北大学博士生导师,主要从事超声波层析成像算法研究。zhanglin158158@163.com

引用本文:

张琳,邵富群,周明. 基于自适应步长双参数正则化算法的超声波过程层析成像图像重建[J]. 计量学报, 2015, 36(1): 48-53.
ZHANG Lin,SHAO Fu-qun,ZHOU Ming. Image Reconstruction of Ultrasonic Process Tomography Based on Fast Two-parameter Regularization Algorithm. Acta Metrologica Sinica, 2015, 36(1): 48-53.

链接本文:

http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2015.01.11 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2015/V36/I1/48

［1］王化祥,唐磊,闫勇. 电容层析成像图像重建的总变差正则化算法［J］.仪器仪表学报,2007,28(11):2014-2018.
［2］张玲玲,王化祥,范文茹. 基于1范数的电阻层析成像图像重建算法［J］.天津大学学报,2011,(9):786-790.
［3］Lei Jing, Shi Liu, Li Zhihong, et al. An image 〖JP〗reconstruction algorithm based on the extended Tikhonov regularization method for electrical capacitance tomography［J］.Measurement,2009,42(3): 368-376.
［4］Landweber L.An iteration formula for fredholm integral equations of the first kind［J］.American Journal of Mathematics,1951,73(3):615-624.
［5］Mou Changhua, Peng Lihui, Yao Danya, et al. Image 〖JP〗reconstruction using a genetic algorithm for electrical capacitance tomography［J］. Tsinghua Science and Technology,2005,10 (5) : 587-592.
［6］Ortiz-Aleman C,Martin R,Gamio J C. Reconstruction of permittivity images from   capacitance tomography data by using very fast simulated annealing［J］.Measurement Science and Technology,2004, 15 (7): 1382-1390.
［7］Warsito W, Fan L S.Neural network    based multi-criterion optimization image reconstruction technique for imaging two- and three-phase flow systems using electrical capacitance tomography［J］.Measurement Science and Technology,2001,12 (12): 2198-2210.
［8］Wang Huaxiang, Tang Lei, Cao Zhang. An image reconstruction algorithm based on total variation   with adaptive mesh refinement for ECT［J］.Flow Measurement and Instrumentation,2007,18(5-6):262-267.
［9］Hansen P C, O’Leary D P. The use of the L-curve in the regularization of discrete ill-posed problems［J］. SIAM Journal on Scientific Computing, 1993, 14(6): 1487-1503.
［10］Hansen P C. Analysis of Discrete Ill-Posed Problems by Means of the L-Curve［J］.SIAM Review, 1992,34(4):561-580.
［11］Johnston P R, Gulrajani R M. Selecting the corner in the L-curve approach to Tikhonov Regularization［J］. IEEE Transactions on Biomedical Engineering,2000,47(9):1293-1296.