Multi-Window Gabor Transform Network for Ground Penetrating Radar B-Scan Image Reconstruction

Abstract

Transmitting and receiving electromagnetic wave signals reflected back to the ground can detect the structure of subsurface defects. However, the imaging process of ground-penetrating radar (GPR) is highly susceptible to interference from complex underground environments, leading to nonlinear attenuation and noise. This makes it challenging to directly locate and identify defect types from raw reflected radar waveform images. Currently, mainstream methods of manual radar signal gain and filtering heavily rely on expert experience, while common end-to-end generative models are typically designed for optical images. This paper proposes a defect-guided Multi-window Gabor Transform Network (MGT-Net) for GPR B-Scan image reconstruction which achieves automatic gain and defect enhancement of raw GPR signals. Firstly, a Multi-window Gabor Transform Module (MGTM) is designed to effectively represent and extract spatial-frequency features of defects at different locations and of various types. Secondly, a defect guidance network (DG-Net) is constructed to accurately direct the reconstruction of defect areas and enhance the saliency and discriminability of defect features. Additionally, we construct a large-scale GPR B-Scan image dataset (GRD) containing 41,613 images across 7 defect categories. Experimental results show the superior performance of MGT-Net, achieving state-of-the-art (SOTA) SSIM of 81.72% ± 3.5% and PSNR of 30.50 ± 0.442.