Clutter Suppression in Ground-Penetrating Radar Images Using an Improved Pix2Pix Model

Authors

  • Zewen Zi School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China
  • Ying Liu School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China
  • Yuxin Ji School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China
  • Yanyan Zang School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China

DOI:

https://doi.org/10.54097/fh9pnb90

Keywords:

Ablation Experiment, CBAM, Clutter Suppression, Ground Penetrating Radar, MS‑SSIM, Pix2Pix

Abstract

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References

[1] Zhao, Y., Yang, X., Qu, X., Lan, T., & Gong, J. (2023). Clutter removal method for GPR based on low rank and sparse decomposition with total variation regularization. IEEE Geoscience and Remote Sensing Letters, 20, 3502605, 1–5. https://doi.org/10.1109/LGRS.2023.3250717. DOI: https://doi.org/10.1109/LGRS.2023.3250717

[2] Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004. https://doi.org/10. 48550/ arXiv. 1611.07004.

[3] Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2018). Residual dense network for image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: https://doi.org/10.1109/CVPR.2018.00262

[4] Huang, X., Liu, J., Yang, F., Qiao, X., Gao, L., Fu, T., & Zhao, J. (2025). Study on GPR image restoration for urban complex road surfaces using an improved CycleGAN. Remote Sensing, 17(5), 823. https://doi.org/10.3390/rs17050823. DOI: https://doi.org/10.3390/rs17050823

[5] Ni, Z.-K., Shi, C., Pan, J., Zheng, Z., Ye, S., & Fang, G. (2022). Declutter-GAN: GPR B-scan data clutter removal using conditional generative adversarial nets. IEEE Geoscience and Remote Sensing Letters, 19, 4023105, 1–5. https://doi.org/10. 1109/ LGRS.2022.3159788. DOI: https://doi.org/10.1109/LGRS.2022.3159788

[6] Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018). CBAM: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV). DOI: https://doi.org/10.1007/978-3-030-01234-2_1

[7] Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003). Multiscale structural similarity for image quality assessment. In Proceedings of the 37th Asilomar Conference on Signals, Systems and Computers (Vol. 2, pp. 1398–1402). https://doi. org/ 10. 1109/ACSSC.2003.1292216. DOI: https://doi.org/10.1109/ACSSC.2003.1292216

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Published

28-09-2026

Issue

Vol. 18 No. 1 (2026)

Section

Articles

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