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ECT Image Reconstruction Based on Parallel Adaptive Residual Network with CBAM |
MA Min,WU Huan |
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China |
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Abstract In order to solve the problem that soft effect in capacitance tomography should lead to low accuracy of reconstructed image, a parallel adaptive residual network with convolutional attention mechanism image reconstruction algorithm is proposed. The parallel adaptive residual module is used to extract the rich feature layer information. The weight coefficients of each channel are adjusted by the squeeze and excitation networks to filter the redundant information. The convolutional attention mechanism is used to learn the channel and space information of the shallow feature. The feature fusion between the convolutional attention mechanism channel and the parallel adaptive residual network is proposed to compensate the lost shallow features and spatial information. Simulation results show that compared with LBP algorithm, Landweber iterative algorithm and 1DCNN algorithm, the improved algorithm can guarantee real-time performance and improve reconstruction quality effectively.
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Received: 27 July 2022
Published: 21 February 2024
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Fund:National Natural Science Foundation of China |
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