最大类间方差法改进的ECT正则化图像重建算法
孙启国,罗光旺,闫晓丹
(北方工业大学 机械与材料工程学院,北京 100144)
摘要:电容层析成像作为油气润滑气液两相流参数检测的主流方法之一,其成像系统具有高度不适定性。研究旨在优化能满足油气润滑系统精确度和实时性要求的电容层析成像图像重建算法,以擅长处理不适定问题的Tikhonov标准正则化算法作为电容层析成像图像重建系统的基础算法,并采用最大类间方差法自适应获得的最优阈值对Tikhonov标准正则化重建的图像进行图像分割,达到修正标准正则化算法过度光滑缺点的目的。结果显示,改进后的算法图像误差减小,图像相关系数增大,表明图像精确度明显提升。
关键词:电容层析成像;图像重建;正则化;最大类间方差法
中图分类号:TB11 文献标志码:A doi:10.3969/j.issn.1006-0316.2018.05.003
文章编号:1006-0316 (2018) 05-0010-05
Improved ECT Regularization Image Reconstruction Algorithm with the OTSU
SUN Qiguo,LUO Guangwang,YAN Xiaodan
( Mechanical and Materials Engineering College, North China University of Technology, Beijing 100144, China )
Abstract:Electrical capacitance tomography is one of the mainstream parameter testing methods for gas-liquid two-phase flow. Its imaging system is highly ill-posed. This study aims to optimize the image reconstruction algorithm of electrical capacitance tomography that can meet the accuracy and real-time requirements of oil-gas lubrication system. The Tikhonov standard regularization algorithm is good at dealing with the ill-posed problem. Taking it as the basic algorithm of the electrical capacitive tomography image reconstruction system, the optimal threshold value is adaptively obtained by OTSU to perform image segmentation on the reconstructed image by the Tikhonov standard regularization algorithm, revising the drawback of being excessively smooth caused by standard regularization algorithm. The results show that the improved algorithm reduces the image error and increase the image correlation coefficient. Image accuracy has been significantly improved.
Key words:electrical capacitance tomography;image reconstruction;regularization;OTSU
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收稿日期:2017-11-24
基金项目:北京市属高等学校人才强教计划项目(PHR201107109)
作者简介:孙启国(1963-),男,山东烟台人,博士,教授,主要研究方向为摩擦学与工业润滑技术、机械系统动力学及其控制。
 

 

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