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| Image Reconstruction of Electrical Capacitance Tomography Based on Deep Wavelet Networks |
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Abstract The image reconstruction algorithm based on deep wavelet network is proposed to address the problem of low reconstruction accuracy due to the "soft field" effect and unsuitable characterization in the image reconstruction process of capacitive tomography. 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. The simulation and experimental results show that the proposed image reconstruction algorithm has higher image reconstruction accuracy and better reconstruction quality. The average relative image error of the reconstructed image is 0.1918, and the average correlation coefficient is 0.9685.
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Received: 22 September 2023
Published: 26 September 2024
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Corresponding Authors:
Li-Feng ZHANG
E-mail: hdlfzhang@126.com
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2024, Vol. 45 
