Electrical resistance tomography with conditional generative adversarial networks

Abstract

Abstract Electrical resistance tomography (ERT) can reconstruct measured field images non-invasively and is therefore widely used in the field of engineering technology. However, blurred reconstructed images and the lack of detailed features limit the application of electrical tomography. In this paper, a reconstruction method based on a conditional generative adversarial network (CGAN) is proposed to mitigate these problems. CGAN, a deep learning algorithm, can generate images based on condition information, making the generated images more stable and trustable. To adapt the training to the characteristics of ERT, the CGAN structure has been improved. In addition, loss judgement is added to the discriminator training to improve the training efficiency. We further propose a data generation method to simulate various conductivity distributions. The method is tested by using a testing set without repetition data in the training set. The results indicate that the method can effectively improve the sharpness of the reconstructed images and the reconstruction of the image details and reduce reconstructed image error. Using real experimental data for reconstruction, the results also show that this method can effectively improve the quality of reconstruction.