为求解电容层析成像(ECT)中的非线性病态反问题,提出了一种基于改进ResNet-18网络的ECT图像重建方法。该网络由多个残差块组成,每个残差块被分组,然后通过组内残差连接,以融合更多的特征尺度。通过MATLAB仿真实验平台构建了流型数据集,利用深度残差网络的非线性映射能力,完成训练集的学习与训练,并利用测试集进行训练效果评价。在此基础上进行了静态实验。仿真与静态实验结果均表明:与LBP、Landweber迭代算法及未改进ResNet-18算法相比,该方法的图像重建质量明显提高,并具有良好的泛化能力。
Abstract
In order to solve the nonlinear ill-posed inverse problem of electrical capacitance tomography (ECT), the reconstruction method based on a kind of improved ResNet-18 network is presented. The network consists of multiple residual blocks, each of which is grouped and then connected by intra-group residuals to fuse more feature scales. The dataset of flow pattern is constructed by MATLAB simulation experimental platform, and the learning and training of the training set are completed by using the nonlinear mapping ability of the deep residual network. And then, the trained network is evaluated with the test set. After that, static experiment is carried out. Both the results of simulation and static experiments show that the quality of ECT image reconstruction of the presented method is significantly improved and has good generalization ability compared with LBP, Landweber iterative algorithm and unimproved ResNet-18 algorithm.
关键词
计量学 /
电容层析成像 /
图像重建 /
深度学习 /
残差网络 /
多相流检测
Key words
metrology;electrical capacitance tomography;image reconstruction;deep learning;residual networks /
multiphase flow detection
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