For the problems of missing edges and obvious artifacts in Electrical Capacitance Tomography (ECT) reconstruction algorithms, an image reconstruction method based on a multiscale dual-channel convolutional neural network is proposed. Firstly, the image reconstructed by Landweber algorithm is input into the convolutional neural network, and four scales are selected for feature extraction. Feature unions are used across the scales to fuse the information of the output layer with feature maps. To improve the imaging accuracy, two frequency channels are designed for the input image. The middle layer of the network consists of two fully convolutional structures. Convolutional layers and jump connections are designed separately for different channels, which greatly improves the network’s ability to extract feature information and reduces the number of feature maps required for each layer. The number of network layers is shallow, which can speed up the network training, prevent the network from falling into local optimum, and ensure the effective transmission of image details. Simulation experiments are carried out for four typical dual media distributions. The edges of the reconstructed image are smoother and the image error is smaller. It effectively resolves the lack of edges in the reconstruction image and reduces the image edge artifacts in the ECT system.

中文翻译：

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基于多尺度双通道卷积神经网络的ECT图像重建算法

针对电容层析成像重建算法中边缘缺失和伪影明显的问题，提出了一种基于多尺度双通道卷积神经网络的图像重建方法。首先，将通过Landweber算法重建的图像输入到卷积神经网络，并选择四个尺度进行特征提取。跨比例使用要素并集将输出图层的信息与要素图融合。为了提高成像精度，为输入图像设计了两个频道。网络的中间层由两个完全卷积的结构组成。卷积层和跳转连接是针对不同通道分别设计的，这大大提高了网络提取特征信息的能力，并减少了每一层所需的特征图的数量。网络层数较浅，可以加快网络训练速度，防止网络陷入局部最优状态，确保有效传输图像细节。针对四种典型的双媒体分布进行了仿真实验。重建图像的边缘更平滑，图像误差更小。它有效地解决了重建图像中边缘不足的问题，并减少了ECT系统中的图像边缘伪影。针对四种典型的双媒体分布进行了仿真实验。重建图像的边缘更平滑，图像误差更小。它有效地解决了重建图像中边缘不足的问题，并减少了ECT系统中的图像边缘伪影。针对四种典型的双媒体分布进行了仿真实验。重建图像的边缘更平滑，图像误差更小。它有效地解决了重建图像中边缘不足的问题，并减少了ECT系统中的图像边缘伪影。