摘要
为提高电磁层析成像(electromagnetic tomography,EMT)技术在金属结构缺陷检测时图像重建的效果,研究了基于迭代硬阈值(iterative hard thresholding,IHT)算法的稀疏成像方法。该文对传统图像重建算法的出发点、计算过程进行分析,再根据金属结构上缺陷分布的稀疏特性,选择稀疏成像方法;结合l_(2)、l_(1)、l_(0)范数约束下解的特点,选择l_(0)范数进行正则化约束解的范围,采用迭代硬阈值算法进行图像重建,并与Landweber迭代算法、Tikhonov正则化算法的图像重建效果进行对比。软件仿真和硬件实验均表明:l_(0)范数约束下的迭代硬阈值稀疏成像算法能够提高金属缺陷的图像重建质量;得到的图像相对误差比Landweber迭代算法、Tikhonov正则化算法降低10%;重建图像所用的时间减少一半。
In order to improve the image reconstruction effect of electromagnetic tomography(EMT)technology in metal structure defect detection,the sparse imaging method based on iterative hard thresholding(IHT)algorithm is studied.This paper analyzes the starting point and calculation process of traditional image reconstruction algorithms,and then selects the sparse imaging method according to the sparse characteristics of the defect distribution on the metal structure.Combining the characteristics of solutions under l_(2),l_(1),and l_(0) norm constraints,select l_(0) norm to regularize the range of constraint solutions,and use iterative hard threshold algorithm for image reconstruction,and compare the image reconstruction effects with Landweber iterative algorithm and Tikhonov regularization algorithm.Both software simulation and hardware experiment show that the iterative hard-threshold sparse imaging algorithm under the constraint of l_(0) norm can improve the image reconstruction quality of metal defects.The relative error of the obtained image is reduced by 10%compared with Landweber iterative algorithm and Tikhonov regularization algorithm.The time it takes to reconstruct the image is reduced by half.
作者
孙春光
何敏
曾星星
冯肖维
SUN Chunguang;HE Min;ZENG Xingxing;FENG Xiaowei(College of Logistics Engineering,Shanghai Maritime University,Shanghai 201306,China;School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China)
出处
《中国测试》
CAS
北大核心
2022年第2期21-26,共6页
China Measurement & Test
基金
国家自然科学基金资助项目(61503241)。
