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 0.0094, and the correlation coefficient is as high as 0.9973, which is a new method and means to study ECT image reconstruction.
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2021, Vol. 42 
