Image Reconstruction of Electrical Capacitance Tomography Based on Improved ResNet-18 Network

ZHANG Li-feng; CHANG En-jian

doi:10.3969/j.issn.1000-1158.2023.09.13

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Acta Metrologica Sinica ›› 2023, Vol. 44 ›› Issue (9) : 1402-1408. DOI: 10.3969/j.issn.1000-1158.2023.09.13

Image Reconstruction of Electrical Capacitance Tomography Based on Improved ResNet-18 Network

ZHANG Li-feng,CHANG En-jian

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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.

Key words

metrology;electrical capacitance tomography;image reconstruction;deep learning;residual networks / multiphase flow detection

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ZHANG Li-feng,CHANG En-jian. Image Reconstruction of Electrical Capacitance Tomography Based on Improved ResNet-18 Network[J]. Acta Metrologica Sinica. 2023, 44(9): 1402-1408 https://doi.org/10.3969/j.issn.1000-1158.2023.09.13

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