Semi-supervised learning-assisted imaging method for electrical capacitance tomography

Despite the enormous potential of the electrical capacitance tomography technology for the process industry, one of the main technological gaps and obstacles that must be overcome is the low accuracy reconstruction. To address this technical challenge, the data-dependent prior is introduced in this study, which is combined with the electrical measurement mechanism and the domain knowledge to reshape the tomographic imaging problem into a new optimization problem. The introduction of the data-dependent prior not only bridges the physical model and the advanced semi-supervised learning technique, but also improves the adaptability of the reconstruction model. A new numerical scheme that integrates the merits of the half-quadratic splitting method and the forward-backward splitting technique with a speedup strategy is proposed to solve the challenging imaging model, leading to the reduced computational burden. A new robust sparse semi-supervised extreme learning machine method that leverages both labeled and unlabeled samples and effectively handles high-dimensional image data is developed to predict the data-dependent prior, and the training is recast into a new optimization problem that is solved by a new numerical method. The novel imaging method achieves the reconstruction paradigm shift and acts as a catalyst for improving imaging quality. The evaluation of a series of reconstruction cases validates that the novel method shows satisfactory performances, outperforming both popular and classical imaging methods in terms of robustness and reconstruction accuracy.