Image Reconstruction Based on Multilevel Densely Connected Network With Threshold for Electrical Capacitance Tomography

Electrical capacitance tomography (ECT) is a real-time monitoring technology for the visualization of industrial dynamic processes. Due to the inherent nonlinearity and ill-posed nature of the ECT inverse problem, achieving fast and accurate image reconstruction remains a great challenge. A novel multilevel densely connected network with channelwise thresholds (MDCN-CW) is proposed, which adopts a deep-learning framework to implement a reconstruction process similar to iterative algorithms. MDCN-CW is a highly condensed framework that achieves efficient information transfer through dense connections between and within subnetworks, and soft thresholding is inserted into the subnetwork as a nonlinear transformation layer to eliminate unimportant features. Matching the purpose and structure of MDCN-CW, a phased strategy is used to train it. Each subnetwork is first trained with a stepwise individual strategy, and network parameters are fine-tuned after all subnetworks are integrated to improve the overall fit of the model. Simulation and experimental results show that, compared with existing deep-learning-based reconstruction methods and traditional algorithms, the proposed ECT image reconstruction network with soft thresholds has the advantages of simple structure, high imaging accuracy, and good generalization ability.

Automated summary

This paper proposes a deep-learning-based electrical capacitance tomography (ECT) image reconstruction network with features such as multilevel dense connections and soft thresholds, achieving simplicity, high imaging accuracy, and good generalization ability.