Ensemble learning-based computational imaging method for electrical capacitance tomography

Abstract

Electrical capacitance tomography (ECT) is a potential measurement technology for industrial process monitoring, but its applicability is generally limited by low-quality tomographic images. Boosting the performance of inverse computing imaging algorithms is the key to improving the reconstruction quality (RQ). Common regularization iteration imaging methods with analytical prior regularizers are less flexible in dealing with actual reconstruction tasks, leading to large reconstruction errors. To address the challenge, this study proposes a new imaging method, including reconstruction model and optimizer. The data-driven regularizer from a new ensemble learning model and the analytical prior regularizer with the focus on the sparsity of imaging objects are combined into a new optimization model for imaging. In the proposed ensemble learning model, the generalized low rank approximations of matrices (GLRAM) method is used to carry out the dimensionality reduction for decreasing the redundancy of the input data and improving the diversity, the extreme learning machine (ELM) serves as a base learner and the nuclear norm based matrix regression (NNMR) method is developed to aggregate the ensemble of solutions. The singular value thresholding method (SVTM) and the fast iterative shrinkage-thresholding algorithm (FISTA) are inserted into the split Bregman method (SBM) to generate a powerful optimizer for the built computational model. Its comparison to other competing methods through numerical experiments on typical imaging targets demonstrates that the developed algorithm reduces reconstruction error and achieves much more improvement in imaging quality and robustness.