Abstract: Electrical capacitance tomography (ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance. To solve the nonlinear and ill-posed inverse problem: image reconstruction of ECT system, this paper proposed a new image reconstruction method based on improved radial basis function (RBF) neural network combined with adaptive wavelet image enhancement. Firstly, an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured. Then, for better image quality, adaptive wavelet image enhancement technique was emphatically analyzed and studied, which be-longs to a space-frequency analysis method and is suitable for image feature-enhanced. Through multi-level wavelet decomposition, edge points of the image produced from RBF network can be determined based on the neighbor-hood property of each sub-band; noise distribution in the space-frequency domain can be estimated based on statis-tical characteristics; after that a self-adaptive edge enhancement gain can be constructed. Finally, the image is re-constructed with adjusting wavelet coefficients. In this paper, a 12-electrode ECT system and a pneumatic convey-ing platform were built up to verify this image reconstruction algorithm. Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement, and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal (PC)/air].

Key words: electrical capacitance tomography, image reconstruction, radial basis function network, wavelet image enhancement

摘要： Electrical capacitance tomography (ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance. To solve the nonlinear and ill-posed inverse problem: image reconstruction of ECT system, this paper proposed a new image reconstruction method based on improved radial basis function (RBF) neural network combined with adaptive wavelet image enhancement. Firstly, an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured. Then, for better image quality, adaptive wavelet image enhancement technique was emphatically analyzed and studied, which be-longs to a space-frequency analysis method and is suitable for image feature-enhanced. Through multi-level wavelet decomposition, edge points of the image produced from RBF network can be determined based on the neighbor-hood property of each sub-band; noise distribution in the space-frequency domain can be estimated based on statis-tical characteristics; after that a self-adaptive edge enhancement gain can be constructed. Finally, the image is re-constructed with adjusting wavelet coefficients. In this paper, a 12-electrode ECT system and a pneumatic convey-ing platform were built up to verify this image reconstruction algorithm. Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement, and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal (PC)/air].

关键词: electrical capacitance tomography, image reconstruction, radial basis function network, wavelet image enhancement