Computational inverse imaging method by machine learning-informed physical model for electrical capacitance tomography

The solution of the imaging inversion problem is an important step in the electrical capacitance tomography developed for process parameter measurements. Many studies have been carried out to improve the reconstruction quality, but the discrepancy between ground-truth imaging prototypes and recovered tomograms is still significant. To address the challenge, the regularization by denoising (RED) is introduced in this work, turning the denoising algorithm into a regularizer. Measurement physics, RED and sparsity prior are coupled into a new imaging model. A new numerical method is developed to solve the established imaging model by integrating the split Bregman algorithm and the forward backward splitting technique. To improve the performance of RED, the multiple output least squares support vector machine is combined with the low-dimensional representation method, and the training problem is solved by a new distributed computational method. The nonnegative matrix factorization method is extended into a new low-dimensional representation method, and a powerful optimizer is developed to solve the model. The performance evaluations clearly imply that the new method achieves more significant reconstruction performance gain and better robustness than popular imaging algorithms. This study improves the measurement physics based imaging method by machine learning techniques, and provides new perspectives and insights into the development of the image reconstruction paradigm.

Automated summary

This study introduces Regularization by Denoising (RED) to improve the reconstruction quality of electrical capacitance tomography imaging. A new numerical method is developed by integrating multiple output least squares support vector machine and low-dimensional representation method to enhance performance. The method outperforms popular imaging algorithms, providing new perspectives for the development of image reconstruction paradigm.