A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Denoising Ground Penetrating Radar Images Using Generative Adversarial Networks
The objective of this study is to improve the visual quality of noisy ground penetrating radar (GPR) images by developing a deep learning network. The dataset includes noisy GPR images that were generated by adding white noise with a coefficient of variation of 1.0, and the original raw GPR images as labels. In addition, a denoising deep learning network was built and trained on the dataset. The experimental results show that the denoising network performs well with high peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values. Furthermore, the denoised GPR images show as much detail as the ground-truth images. This study shows that the denoising network significantly denoises the GPR images.
Denoising Ground Penetrating Radar Images Using Generative Adversarial Networks
The objective of this study is to improve the visual quality of noisy ground penetrating radar (GPR) images by developing a deep learning network. The dataset includes noisy GPR images that were generated by adding white noise with a coefficient of variation of 1.0, and the original raw GPR images as labels. In addition, a denoising deep learning network was built and trained on the dataset. The experimental results show that the denoising network performs well with high peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values. Furthermore, the denoised GPR images show as much detail as the ground-truth images. This study shows that the denoising network significantly denoises the GPR images.
Denoising Ground Penetrating Radar Images Using Generative Adversarial Networks
Lecture Notes in Civil Engineering
Duc Long, Phung (editor) / Dung, Nguyen Tien (editor) / Hoang, Ngoc Quy (author) / Kang, Seonghun (author) / Lee, Jong-Sub (author)
International Conference on Geotechnics for Sustainable Infrastructure Development ; 2023 ; Hanoi, Vietnam
Proceedings of the 5th International Conference on Geotechnics for Sustainable Infrastructure Development ; Chapter: 159 ; 2343-2349
2024-07-11
7 pages
Article/Chapter (Book)
Electronic Resource
English
Ground Penetrating Radar: Images of Subsurface Characteristics
| British Library Conference Proceedings | 1993
| Springer Verlag | 2010