|
|
Image Reconstruction of ECT Based on Multi-scale Adaptive Network |
ZHANG Lifeng,CHANG Enjian |
Department of Automation, North China Electric Power University, Baoding, Hebei 071003, China |
|
|
Abstract In order to solve the nonlinear ill-posed inverse problem of electrical capacitance tomography (ECT), a multi-scale adaptation network (MSANet) model is proposed, which achieves the fusion of multi-scale features in a more fine-grained dimension and has a relatively small number of model parameters.By constructing a tree structure within a single residual block to form a multi-scale feature fusion module, MSANet achieves more robustness and lower computational parameters. Furthermore,by adding an adaptive spatial threshold module, the reconstruction accuracy of the images is further improved. Compared with linear back projection (LBP) algorithm, Landweber iterative algorithm,and commonly used deep learning methods, this method has the smallest average relative error and the largest average correlation coefficient,with 0.181 and 0.967, respectively.
|
Received: 09 June 2023
Published: 05 September 2024
|
|
|
|
|
[6] |
严春满, 穆哲, 张道亮, 等. 基于改进Landweber算法的ECT图像重建 [J]. 传感技术学报, 2019, 32(10): 1522-1526.
|
[5] |
张立峰, 张明. 一种电容层析成像图像重建优化算法 [J]. 计量学报, 2021, 42(9): 1155-1159.
|
[3] |
马敏, 郭鑫. 基于改进SR3模型算法的ECT图像重建研究 [J]. 计量学报, 2023, 44(1): 95-102.
|
[10] |
马敏, 孙颖, 范广永. 基于深度信念网络的ECT图像重建算法 [J]. 计量学报, 2021, 42(4): 476-482.
|
[7] |
XU Y, PEI Y and DONG F. An adaptive Tikhonov regularization parameter choice method for electrical resistance tomography[J]. Flow Meas Instrum , 2016 , 50: 1-12.
|
[4] |
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, 2010, 44(3): 165-273.
|
[11] |
YANG X, ZHAO C, LI Y, et al. Big data driven U-Net based electrical capacitance image reconstruction algorithm [C]//2019 IEEE International Conference on Imaging Systems and Techniques(IST). Abu Dhabi, UAE, 2019.
|
|
YAN C M, MU Z, ZHANG D L, et al. ECT image reconstruction based on improved Landweber algorithm [J]. Chinese Journal of Sensors and Actuators, 2019, 32(10): 1522-1526.
|
[9] |
ZHENG J and PENG L H. A deep learning compensated back projection for image reconstruction of electrical capacitance tomography[J]. IEEE Sensors Journal, 2020, 20(9): 4879-4890.
|
[15] |
YANG W Q. Hardware design of electrical capacitance tomography systems [J]. Meas Sci Technol, 1996, 7(3): 225-232.
|
|
MA M, GUO X. Research on ECT image reconstruction based on improved SR3 model algorithm [J]. Acta Metrologica Sinica, 2023, 44(1): 95-102.
|
|
ZHANG L F, ZHANG M. An optimized algorithm for image reconstruction of electrical capacitance tomography [J]. Acta Metrologica Sinica,2021, 42(9): 1155-1159.
|
|
MA M, SUN Y, FAN G Y. ECT image reconstruction algorithm based on depth belief network [J]. Acta Metrologica Sinica, 2021, 42(4): 476-482.
|
[13] |
ZHU H, SUN J, LONG J, et al. Deep image refinement method by hybrid training with images of varied quality in electrical capacitance tomography [J]. IEEE Sensors Journal, 2021, 21(5): 6342-6355.
|
[1] |
ZHU L, JIANG Y, Li Y, et al. Conductivity prediction and image reconstruction of complex-valued multi-frequency electrical capacitance tomography based on deep neural network [J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-10.
|
[2] |
张立峰, 戴力. 基于鲁棒正则化极限学习机的电容层析成像图像重建 [J]. 计量学报, 2022, 43(8): 1044-1049.
|
|
ZHANG L F, DAI L. Image reconstruction of electrical capacitance tomography based on robust regularization limit learning machine [J]. Acta Metrologica Sinica, 2022, 43(8): 1044-1049.
|
[8] |
WAMG Z, ZHANG L F, ZHANG B X, et al. Image reconstruction based on multilevel densely connected network with threshold for electrical capacitance tomography[J]. IEEE Sensors Journal, 2022, 22(22): 21996-22007.
|
[12] |
LI F, TAN C, DONG F, et al. V-Net deep imaging method for electrical resistance tomography[J].IEEE Sensors Journal,2020, 20(12): 6460-6469.
|
[14] |
FU R, WANG Z, ZHANG X, et al. A regularization-guided deep imaging method for electrical impedance tomography[J]. IEEE Sensors Journal, 2022, 22(9): 8760-8771.
|
[16] |
ZHU H, SUN J, XU L, et al. Permittivity reconstruction in electrical capacitance tomography based on visual representation of deep neural network [J]. IEEE Sensors Journal, 2020, 20(9): 4803-4815.
|
[17] |
WANG S, XU C, LI J, et al. An instrumentation system for multi-parameter measurements of gas-solid two-phase flow based on capacitance-electrostatic sensor[J]. Measurement, 2016, 94: 812-827.
|
|
|
|