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.

中文翻译：

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使用域自适应生成对抗网络的 GPR 图像端到端频率增强框架

探地雷达 （GPR） 提供无损地下成像，但在频率和穿透深度之间需要权衡：高频在深度有限的情况下产生更好的分辨率，而低频在细节减少的情况下穿透得更深。本文介绍了一种使用域自适应生成对抗网络的 GPR 图像的新型频率增强方法。所提出的端到端框架集成了域自适应模块 （DAM） 和频率增强模块 （FEM），以解决频率分辨率权衡和域差异。DAM 对齐模拟和真实的低频 GPR 数据，从而通过 FEM 实现有效的频率增强。由于模拟和真实世界 GPR 数据之间的信号特性存在固有差异，直接在模拟数据上训练的模型应用于真实世界场景通常会导致性能下降和物理一致性丧失，因此域适应对于弥合这一差距至关重要。通过减少域差异和确保特征一致性，该框架可以生成具有增强清晰度和细节的高频 GPR 图像。广泛的实验表明，该方法显著提高了图像质量、目标检测和定位精度，优于最先进的方法，并显示出在地下成像应用中的巨大潜力。