基于深层小波网络的电容层析成像图像重建OA北大核心CSTPCD

The image reconstruction algorithm of electrical capacitance tomography(ECT)based on deep wavelet network is presented.The Landweber algorithm is used to generate the initial reconstructed image as the input of the network.Taking the U-Net deep convolutional neural network model as the backbone model,the wavelet transform is introduced into the upper and lower sampling layers to extract the features of different levels and the high-frequency feature transfer channel is built through a skip connection to retain more detailed information and make full use of global and local information features in the feature map.Both simulation and experimental results show that the proposed image reconstruction algorithm has higher image reconstruction accuracy.The average relative image errors of simulated and static experimental reconstructed images were 0.191 8 and 0.657 0,respectively,with average correlation coefficients of 0.968 5 and 0.816 9.