  The clutter in the ground-penetrating radar (GPR) radargram disguises or
distorts subsurface target responses, which severely affects the accuracy of
target detection and identification. Existing clutter removal methods either
leave residual clutter or deform target responses when facing complex and
irregular clutter in the real-world radargram. To tackle the challenge of
clutter removal in real scenarios, a clutter-removal neural network (CR-Net)
trained on a large-scale hybrid dataset is presented in this study. The CR-Net
integrates residual dense blocks into the U-Net architecture to enhance its
capability in clutter suppression and target reflection restoration. The
combination of the mean absolute error (MAE) loss and the multi-scale
structural similarity (MS-SSIM) loss is used to effectively drive the
optimization of the network. To train the proposed CR-Net to remove complex and
diverse clutter in real-world radargrams, the first large-scale hybrid dataset
named CLT-GPR dataset containing clutter collected by different GPR systems in
multiple scenarios is built. The CLT-GPR dataset significantly improves the
generalizability of the network to remove clutter in real-world GPR radargrams.
Extensive experimental results demonstrate that the CR-Net achieves superior
performance over existing methods in removing clutter and restoring target
responses in diverse real-world scenarios. Moreover, the CR-Net with its
end-to-end design does not require manual parameter tuning, making it highly
suitable for automatically producing clutter-free radargrams in GPR
applications. The CLT-GPR dataset and the code implemented in the paper can be
found at https://haihan-sun.github.io/GPR.html.
