基于极限学习机求解正问题的 ECT 图像重建

在电容层析成像(ECT)图像重建迭代类算法中,通常采用线性正问题求解,以加快重建速度,由此产生重建误差。针对这一问题,提出了基于极限学习机(ELM)的非线性ECT正问题求解方法,ELM网络输入为介电常数分布,其输出为预测的电容测量值。将该方法与传统的Landweber迭代算法相结合构成ELM-Landweber迭代算法进行图像重建。为使样本具有较好的代表性,物体分布位置及大小均随机生成,并计算相应的归一化电容值作为ELM网络训练及测试样本,对ELM-Landweber迭代算法进行了仿真与静态实验,并与传统Landweber迭代算法进行比较。实验结果表明,相较于传统Landweber迭代算法,采用ELM-Landweber迭代算法,其算法收敛速度显著提高,重建图像质量得到明显改善。训练样本的平均图像相对误差由0.728减小至0.504,测试样本的平均图像相对误差由0.596减小至0.475。

Image reconstruction for electrical capacitance tomography based on forward problem solution using extreme learning machine

For the iterative image reconstruction algorithm of electrical capacitance tomography (ECT), linear forward problem solution is usually adopted to speed up image reconstruction. However, image reconstruction error is inevitably produced. In this paper, a nonlinear forward problem solution based on extreme learning machine (ELM) of ECT is proposed. The inputs and outputs of ELM network are permittivity distribution and predicted capacitance measurements, respectively. Image reconstruction is carried out based on the combination of the presented method and conventional Landweber iterative algorithm, which is named as ELM-Landweber iterative algorithm. In order to make the samples more representative, the distribution positions and sizes of objects in each phantom are randomly generated, and the corresponding normalized capacitance values are calculated as ELM network training and test samples. Simulation and static experiments are conducted for ELM-Landweber iterative algorithm and the reconstructed images are compared with those of conventional Landweber iterative algorithm. Experimental results show that the convergence speed of ELM-Landweber iterative algorithm is significantly enhanced, and the quality of the reconstructed image is obviously improved compared with conventional Landweber iterative algorithm. The average image relative error of training samples and test samples decreases from 0. 728 to 0. 504 and from 0. 596 to 0. 475, respectively.