SRGAN-based sparse reconstruction of defects in dielectric materials for microwave detection

Dielectric materials may occur during the manufacturing and service process due to manufacturing defects or complex service environments, which seriously affects the performance, microwave nondestructive testing is an effective means to assess the structural integrity of dielectric materials, but the contradiction between the demand for high-resolution imaging and the difficulty of data processing and detection efficiency needs to be resolved. Image sparse reconstruction algorithms provide a possibility to solve this problem, among which SRGAN (Super-Resolution Generative Adversarial Network) model is excellent in sparse reconstruction. In this paper, SRGAN was used for microwave imaging sparse reconstruction, and the SRGAN network was improved for the characteristics of microwave images, and the effectiveness of the improved algorithm in the sparse reconstruction of defects in dielectric materials was verified through comparative analysis. The results showed that the improved SRGAN can significantly improved the microwave image quality and accurately restore the defect details, which provides strong support for the safety assessment and service life prediction of dielectric material structures.