End‐to‐end frequency enhancement framework for GPR images using domain‐adaptive generative adversarial networks

Ground‐penetrating radar (GPR) offers nondestructive subsurface imaging but suffers from a trade‐off between frequency and penetration depth: High frequencies yield better resolution with limited depth, while low frequencies penetrate deeper with reduced detail. This paper introduces a novel frequency enhancement method for GPR images using domain‐adaptive generative adversarial networks. The proposed end‐to‐end framework integrates a Domain Adaptation Module (DAM) and a Frequency Enhancement Module (FEM) to address frequency‐resolution trade‐offs and domain discrepancies. The DAM aligns simulated and real low‐frequency GPR data, enabling effective frequency enhancement by the FEM. Due to inherent differences in signal characteristics between simulated and real‐world GPR data, directly applying models trained on simulated data to real‐world scenarios often results in performance degradation and loss of physical consistency, making domain adaptation essential for bridging this gap. By reducing domain discrepancies and ensuring feature consistency, the framework generates high‐frequency GPR images with enhanced clarity and detail. Extensive experiments show that the method significantly improves image quality, target detection, and localization accuracy, outperforming state‐of‐the‐art approaches and demonstrating strong potential for subsurface imaging applications.