基于双参数模型的ECT图像重构混合算法

摘要

To improve the image reconstruction speed and quality,a hybrid algorithm based on two-parameter model for electrical capacitance tomography（ECT） image reconstruction is proposed.Morozov discrepancy principle is used to choose Tikhonov regularization parameter which states that the regularization parameter should be chosen such that the error due to the regularization is equal to the error due to the observation data.To obtain an optimal regularization parameter rapidly,two-parameter model is derived based on Morozov discrepancy equation and further combined with Newton method.Simulation results show that the speed of image reconstruction is improved and the quality of the reconstructed image is better than other image reconstruction algorithms such as linear back-projection（LBP）,Landweber algorithm and L-curve method.%为了提高电容层析成像重构图像的精度和速度,本文提出了一种基于双参数模型的电容层析成像图像重构混和算法。该算法利用Morozov偏差原理确定Tikhonov正则参数,能使正则参数的选取与初始数据的误差相匹配,同时基于Morozov偏差方程导出了一种双参数模型,并进一步与牛顿法相结合用于快速得到最优的正则参数。数值实验表明：与线形反投影算法（LBP）、Landweber迭代算法和L-曲线法相比,所提出的混合算法具有图像重构速度快、精度高的优点。