基于深度信念网络的ECT图像重建算法

doi:10.3969/j.issn.1000-1158.2021.04.12

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Abstract In order to improve the quality of image reconstruction, in view of the complexity and variety of capacitance data in electrical capacitance tomography (ECT) and the nonlinear relationship between capacitance data and dielectric constant, a deep belief network (DBN) was proposed.The reconstruction algorithm of DBN used the deep nonlinear network structure of DBN to realize the nonlinear relationship between the capacitance value and the gray value of the reconstructed image.The DBN was improved and the adaptive step size (AS) was introduced into the contrastive divergence (CD) algorithm to solve the problem of finding the global optimum with fixed step size and to improve the image quality.In the fine-tuning stage, quasi-Newton method was used to accelerate the convergence speed and reduce the training time.The simulation experiment was carried out on COMSOL 5.3 software, and the image was reconstructed by MATLAB 2014a.The experimental results showed that: DBN can effectively reconstruct the image, and is better than the traditional algorithm; the improved DBN training time is shortened by 5.51s, the image error is as low as 0.0094, and the correlation coefficient is as high as 0.9973, which is a new method and means to study ECT image reconstruction.

Key words： metrology electrical capacitance tomography deep belief network;image reconstruction COMSOL

Received: 22 April 2019 Published: 20 April 2021

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TB937

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	Articles by authors
	MA Min
	SUN Ying
	FAN Guang-yong

Cite this article:

MA Min,SUN Ying,FAN Guang-yong. ECT Image Reconstruction Algorithm Based on Depth Belief Network[J]. Acta Metrologica Sinica, 2021, 42(4): 476-482.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.04.12 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I4/476

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