Image reconstruction of electromagnetic tomography based on generative adversarial network with spectral normalization and improved dung beetle optimization algorithm

Electromagnetic tomography (EMT) has great application potential in fields such as industrial inspection. However, currently, EMT image reconstruction has problems of being highly nonlinear and ill-posed, resulting in artifacts in reconstructed images and uncertainties in quality, detail accuracy, and robustness. To address these challenges, a deep learning model STDBOGAN (generative adversarial network based on spectral normalization, two timescale update rule, and improved dung beetle optimization algorithm) based on generative adversarial networks is proposed. STDBOGAN introduces spectral normalization and two timescale update rules to stabilize the training process and avoid training instability and gradient problems. The improved dung beetle optimization algorithm automatically adjusts network hyper-parameters to improve image reconstruction accuracy. A dataset is established through simulation software. Ablation studies are conducted on the network before and after improvement, and simulations and metal physical experiments are carried out to compare STDBOGAN to UNet3+, DeepLabv3+, PSPNet, Segmenter, and SegRefiner networks. Experiments show that STDBOGAN has the best performance, anti-noise, and generalization abilities, and has made contributions to improving the quality of electromagnetic tomography image reconstruction.