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.
ZHANG L F, WANG H X. Electrical capacitive tomography image reconstruction algorithm based on wavelet neural network ［J］. Proceedings of the CSEE, 2008, 28 (35): 39-43.
［9］
TONG G W, LIU SH, CHEN H Y, et al. Regularization iteration imaging algorithm for electrical capacitance tomography ［J］. Measurement Science and Technology, 2018, 29 (3): 035403-035403.
MA M, ZHANG C X, LU C C, et al, ECT image processing based on wavelet transform ［J］. Journal of Central South University, 2016, 47 (6): 1947-1952.
LEI J, LIU Q B, WANG X Y. Computational inverse imaging method by machine learning-informed physical model for electrical capacitance tomography ［J］. Journal of Computational Science, 2022,:57
［11］
WANG H M, HU H L, WANG L J, et al. Image reconstruction for an Electrical Capacitance Tomography (ECT) system based on a least squares support vector machine and bacterial colony chemotaxis algorithm ［J］. Flow Measurement and Instrumentation, 2012, 27: 59-66.
WANG H X, YANG W Q. Advances in electrical capacitive process imaging techniques ［J］. Journal of Instrumentation, 2000, 21(1): 4-7+11.
［10］
ZHENG J, PENG L H. An Autoencoder-Based Image Reconstruction for Electrical Capacitance Tomography ［J］. IEEE Sensors Journal, 2018, 18 (13): 5464-5474.
MA M, SUN Y, FAN G Y. Electrical capacitance tomography image reconstruction algorithm based on deep belief network ［J］. Acta Metrologica Sinica, 2021, 42 (4): 476-482.
ZHANG L F, WANG H R. Electrical capacitive tomography image reconstruction based on convolutional neural network and finite element simulation ［J］. Journal of System Simulation, 2022, 34 (4): 712-718.
YAN C M, MU Z, An improved gadient acceleration Landweber algorithm and its application in ECT image reconstruction ［J］. Journal of Electronic Measurement and Instrumentation, 2021, 35 (6): 169-175.
［16］
CUI Z Q, WANG Q, XUE Q, et al. A review on image reconstruction algorithms for electrical capacitance/resistance tomography ［J］. Sensor Review, 2016, 36 (4): 429-445.
ZHANG L F, DAI L. ECT image reconstruction based on extreme learning machine to solve positive problems ［J］. Journal of Instrumentation, 2021, 42 (10): 64-71.
ZHENG J, LI J K, LI Y, et al. A benchmark dataset and deep learning-based image reconstruction for electrical capacitance tomography ［J］. Sensors, 2018, 18 (11): 3701-3701.
MA M, LIU Y F, WANG L. Research on capacitive tomography lubrication oil monitoring method based on improved semi-threshold algorithm ［J］. Propulsion technology, 2021, 42 (11): 2590-2599.
［19］
WAN G, FANG H B, WANG D C, et al. Ceramic tile surface defect detection based on deep learning ［J］. Ceramics International, 2022, 48 (8): 11085-11093.
［7］
GAMIO J C, ORTIZ-ALEMAN C, MARTIN R. Electrical capacitance tomography two-phase oil-gas pipe flow imaging by the linear back-projection algorithm ［J］. Geofísica Internacional, 2005, 44 (3): 265.
LEI J, LIU Q B. Self-paced learning assisted regularization reconstruction method with data-adaptive prior for electrical capacitance tomography［J］. Expert Systems with Applications, 2022, 191: 116296-.
［5］
MARASHDEH Q, WARSITO W, FAN L S, et al. Nonlinear forward problem solution for electrical capacitance tomography using feed-forward neural network ［J］. IEEE Sensors Journal, 2006, 6 (2): 441-449.
［8］
YANG W Q, SPINK D M, YORK T A, et al. An image reconstruction algorithm based on Landweber's iteration method for electrical capacitance tomography ［J］. Measure-ment Science and Technology, 1999, 10 (11): 1065-1069.
2024, Vol. 45 
