Bilevel reinforcement learning imaging method for electrical capacitance tomography

Despite demonstrating considerable promise as a tomography technology for multiphase flow parameter measurements, electrical capacitance tomography is constrained by the inherent suboptimal image reconstruction quality. In order to fully harness its potential, the image reconstruction problem is modeled as a new bilevel fractional optimization problem. This new model integrates the advantages of bilevel optimization and fractional optimization, takes into account the inaccuracy of the measurement model and measurement data, fuses measurement principles with deep learning, learns the model parameters adaptively, achieves the multi-source information fusion, mitigates the ill-posed property of the image reconstruction problem, improves the automation and robustness of the model, and enhances the model’s ability to handle complex measurement scenarios. In order to augment image priors and improve the comprehensive reconstruction performance, based on the regularization by denoising, deep convolutional neural network, as an efficient denoiser, is integrated into the reconstruction model. Following the algorithm unfolding principle, we convert the proposed bilevel fractional optimization problem into a single-level nonlinear optimization problem, which effectively handles the nested structure between the upper and lower level optimization problems and reduces the computational complexity. A new optimizer is proposed to solve this transformed optimization problem. It integrates reinforcement learning and differential evolution algorithm, and is able to adaptively adjust the algorithm parameters by leveraging the interaction and feedback between the parameter configures and the computational results, thus improving the performance of the algorithm and the quality of the optimal solution. Empirical evaluations demonstrate that our novel approach not only yields enhanced imaging quality but also exhibits superior noise resilience when benchmarked against widely-adopted imaging algorithms, while maintaining consistent performance across various scenarios. Our study offers a holistic solution for improving the overall efficacy of image reconstruction tasks by the synergistic fusion of supervised learning methodologies and optimization principles. This fusion not only maximizes the capabilities of the advanced measurement technology but also unlocks its full potential for achieving high-quality reconstruction results.