Acta Metrologica Sinica  2024, Vol. 45 Issue (5): 678-684    DOI: 10.3969/j.issn.1000-1158.2024.05.10
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Image Reconstruction of Electrical Capacitance Tomography Based on Multi-scale Densely Connected Network
ZHANG Lifeng,CHANG Enjian
Department of Automation, North China Electric Power University, Baoding, Hebei 071003, China
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Abstract  In order to solve the nonlinear ill-posed inverse problem in electrical capacitance tomography (ECT), a multiscale dense connection network (multi-scale densely connected network,MD-Net) model is proposed. The model consists of a multiscale feature fusion module and a densely connected block to further improve the reconstruction accuracy of images by fusing multiscale features. A flow-type data set is constructed by the MATLAB simulation experiment platform, and the learning and training of the training set are completed by using the nonlinear mapping ability of the densely connected network. The training effect is evaluated by using the test set. Static experiments are conducted on this basis. The simulation and static experiments results show that the method has the highest reconstruction accuracy, good noise immunity, and generalization ability compared with LBP, Landweber iterative algorithm, and other deep learning methods.
Key wordstwo-phase flow measurement      electrical capacitance tomography      image reconstruction      deep learning      densely connected network     
Received: 14 November 2022      Published: 23 May 2024
PACS:  TB937  
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ZHANG Lifeng
CHANG Enjian
Cite this article:   
ZHANG Lifeng,CHANG Enjian. Image Reconstruction of Electrical Capacitance Tomography Based on Multi-scale Densely Connected Network[J]. Acta Metrologica Sinica, 2024, 45(5): 678-684.
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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.05.10     OR     http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I5/678
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