# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
## 1 Introduction
[14] proposed a rebar clutter suppression method based on range migration compensation and low-rank sparse decomposition with total variation regularization for through-the-wall radar (TWR). The method locates rebars using Hough transform, transforms rebar echoes into a low-rank subspace via range migration compensation, and extracts target signals through inverse range migration. Numerical simulations and experiments demonstrate its superiority in a target-to-clutter ratio (TCR). However, these traditional approaches often suffer from inefficiencies in processing large-scale data and require expert interpretation, limiting their practical applicability. With the advancement of deep learning technology, new opportunities have emerged for GPR-based rebar defect identification in underground structure monitoring, making it a promising trend for the future. Deep learning models enable the automatic recognition and classification of underground structural features, such as rebars and voids, significantly improving data processing speed, accuracy, and reliability. In particular, under strong rebar signal interference, these models can learn distinctive signal features from large datasets, effectively separating rebar and void signals. However, many existing deep learning-based methods rely on supervised learning, which requires a large amount of paired training data--data that are often difficult to obtain in real-world applications. [PERSON] et al. [15] proposed a background filtering algorithm based on the basis pursuit to eliminate dominant linear features and extraneous pattern noise in B-scan images. They then trained a pre-trained AlexNet network model with a 2D grid image composed of several B and C scan images to detect and classify four different types of cavities. [PERSON] et al. [16] considered the statistical distribution of GPR data and automatically determined the amplitude of feature enhancement to enhance the target signal's features, improving the identification accuracy of B-scan images. [PERSON] et al. [17] proposed a supervised approach for rebar clutter removal and defect echo enhancement, achieving both efficient clutter suppression and precise defect reconstruction. Using this approach, the accuracy of identifying defects under rebar increased from 0.208 to 0.850. Despite these advances, supervised methods remain constrained by the availability of high-quality labeled data, making unsupervised learning an attractive alternative. Deep generative models have demonstrated remarkable capabilities in cross-domain feature learning, effectively modeling complex data distributions to achieve high-quality feature transformation and mapping between different visual domains. Their ability to generate realistic transformations makes them particularly suitable for unsupervised learning, where paired training data are often unavailable [18]. This enables effective suppression of rebar clutter while preserving defect signals in GPR images. [PERSON] et al. [19] proposed an unsupervised rebar clutter elimination network (RCE-GAN) designed for detecting void in tunnel linings. The network comprises two sets of generators and discriminators, and by incorporating an attention module and a dilation center component, it enhances the effectiveness of rebar clutter removal. [PERSON] et al. [20] proposed a rebar clutter suppression approach based on GANs to suppress rebar signals in different defect structures. By incorporating four contrasting encoders and two similarity encoders, they effectively suppressed rebar responses and precisely reconstructed defect signals. [PERSON] et al. [21] proposed a VAE and RefineNet network algorithm based on a hybrid loss function for GPR clutter suppression and target imaging. The algorithm has a strong capability to capture detailed features and can significantly improve the quality of target imaging. [PERSON] et al. [22] proposed a DR-GAN network for clutter suppression in GPR B-scan images.