Res2-UNet++: a deep learning image post-processing method for electrical resistance tomography

The monitoring of multiphase flow distribution in industrial processes in order to optimize production presents a challenge. Electrical resistance tomography (ERT) is a technique used to visualize the inner distribution of multiphase flow. Image reconstruction plays a vital role in ERT. However, the nonlinearity and ill-posedness of inverse problems make image reconstruction in ERT difficult. The development of advanced imaging algorithms has attracted much interest for this purpose. In this work, an improved U-shaped deep learning model is proposed, which combines the advantages of the multi-scale feature extraction of UNet++ and the residual feature fusion of Res2Net. The network is designed to post-process the pre-reconstruction results of traditional ERT image-reconstruction methods, combining the generalization ability of the model-based methods and the flexible feature-extraction advantage of deep learning methods. The post-processing includes super-resolution, image denoising and artifact removal. Simulations and experiments are designed to verify the generalization ability and effectiveness of the proposed post-processing 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 in ERT, making it a robust solution for applications.