Res2-UNet++: A Deep Learning Image Post-Processing Method for Electrical Resistance Tomography

Abstract

It is challenging to monitor the multiphase flow distribution in the industrial processes in order to optimize the production. Electrical resistance tomography (ERT) can be used to visualize the inner distribution of multiphase flow. The image reconstruction plays a vital role in ERT. However, the nonlinearity and ill-posedness of inverse problem make the image reconstruction of ERT a challenge, and the development of advanced imaging algorithm has attracted much attention in the past. In this work, an improved U-shaped deep learning model is proposed, which combines the advantages of multi-scale feature extraction of UNet++ and residual feature fusion of Res2Net. The network is used to post-process the prereconstruction result of traditional ERT image reconstruction methods, where the generalization ability of the traditional methods and the flexible feature extraction advantage of deep learning methods can be combined. Simulations and experiments are designed to verify the generalization ability and the effectiveness of the proposed model. Both simulation and experimental results show that the proposed U-shaped network approach outperforms other deep learning methods, and the proposed deep learning model is fit for post-processing tasks of ERT.