基于并联自适应残差网络与CBAM的ECT图像重建

doi:10.3969/j.issn.1000-1158.2024.02.11

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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.

Key words： multiphase flow measurement electrical capacitance tomography;image reconstruction;parallel adaptive residual network;convolution block attention module

Received: 27 July 2022 Published: 21 February 2024

PACS:

TB937

Fund:National Natural Science Foundation of China

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	MA Min
	WU Huan

Cite this article:

MA Min,WU Huan. ECT Image Reconstruction Based on Parallel Adaptive Residual Network with CBAM[J]. Acta Metrologica Sinica, 2024, 45(2): 214-221.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.02.11 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I2/214

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