# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
## 5 Conclusions  
This study proposes an unsupervised generative network model based on VAEs and GANs to suppress multiple reflection interference signals caused by rebar in GPR data. The model transforms GPR images with rebar interference into clutter-free representations, effectively reducing rebar clutter and enhancing void defect signals. By incorporating a channel and spatial attention (CSA) module, the model significantly improves the accuracy and integrity of void defect reconstruction while suppressing rebar-induced noise. Comparative experiments with baseline models demonstrate that our approach achieves superior performance in reducing clutter and preserving void defect signals, as evaluated by RMSE, PSNR, and SSIM. Ablation studies further confirm the effectiveness of the CSA module, with its inclusion leading to a reduction in RMSE from 5.2327 to 5.0145, an increase in PSNR from 25.7002 to 27.3702, and an improvement in SSIM from 0.8496 to 0.8761, highlighting its role in enhancing reconstruction quality. Additionally, experiments under different rebar arrangement conditions validate the robustness of our method. Finally, validation using real GPR data from the Husa tunnel demonstrates that our approach effectively enhances void defect visibility and improves GPR data interpretability in practical applications.  
Although this study employs real GPR data from the Husa tunnel, the proposed method is designed to address the widely existing issue of rebar interference in GPR imaging. Since rebar clutter is a common challenge in the GPR inspection of reinforced concrete structures, our approach is not limited to a specific tunnel but has broader applicability. Future research will focus on improving the model's adaptability to different GPR devices, antenna configurations, and structural conditions, ensuring its robustness across diverse engineering scenarios. Expanding the training dataset with varied GPR system frequencies, structural compositions, and environmental factors will further enhance its generalization ability. Additionally, integrating domain adaptation techniques and adaptive learning mechanisms will improve transferability to new datasets and enable real-time applications. These advancements will strengthen the model's effectiveness, making it applicable to a wider range of infrastructure inspections, including tunnels, bridges, and other reinforced concrete structures.  
Conceptualization, C.L. and Q.M.; methodology, C.L., Q.M. and Y.W.; software, Q.M.; validation, X.Y. and H.L.; formal analysis, Q.M.; investigation, X.Y.; data curation, L.W., H.L. and X.Y; writing--original draft preparation, Q.M. and Y.W.; writing--review and editing, Q.M. and L.W.; supervision, C.L.; project administration, C.L. and L.W.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant NO. 62263015), and funded by Yunnan Provincial Key R&D Program (grant NO. 202203 AA080006).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Authors [PERSON] and [PERSON] were employed by the company Yunnan Aerospace Engineering Geophysical Detecting Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest