GPR Noise Reduction Network Based on Multi-Domain Constrained TransUNet

Deep learning has been widely applied to denoising ground-penetrating radar (GPR) signals. However, most existing methods lack physical constraints consistent with GPR data characteristics, especially in the frequency domain, leading to the loss of weak reflections and blurred reconstruction. Conventional networks also treat GPR denoising as a generic image restoration task without explicit weak-signal enhancement. To address these issues, this paper proposes a frequency-domain multi-scale loss function to introduce physical constraints into network training. Combined with traditional loss functions, the proposed method effectively improves the fidelity of weak reflection recovery. A multi-domain constrained TransUNet is further developed for GPR noise reduction. Experiments on synthetic data and field GPR data demonstrate that the proposed method achieves stronger robustness and competitive denoising performance.