GPR B-Scan Image Denoising via Multi-Scale Convolutional Autoencoder with Data Augmentation
发布时间：2021-06-28
点击次数：
影响因子：2.412
DOI码：10.3390/electronics10111269
发表刊物：Electronics
关键字：ground-penetrating radar (GPR); image denoising; convolutional autoencoder; generative adversarial network; data augmentation
摘要：Ground-penetrating radar (GPR), as a non-invasive instrument, has been widely used in civil engineering. In GPR B-scan images, there may exist random noise due to the influence of the environment and equipment hardware, which complicates the interpretability of the useful information. Many methods have been proposed to eliminate or suppress the random noise. However, the existing methods have an unsatisfactory denoising effect when the image is severely contaminated by random noise. This paper proposes a multi-scale convolutional autoencoder (MCAE) to denoise GPR data. At the same time, to solve the problem of training dataset insufficiency, we designed the data augmentation strategy, Wasserstein generative adversarial network (WGAN), to increase the training dataset of MCAE. Experimental results conducted on both simulated, generated, and field datasets demonstrated that the proposed scheme has promising performance for image denoising. In terms of three indexes: the peak signal-to-noise ratio (PSNR), the time cost, and the structural similarity index (SSIM), the proposed scheme can achieve better performance of random noise suppression compared with the state-of-the-art competing methods (e.g., CAE, BM3D, WNNM).
第一作者：罗佳斌
论文类型：期刊论文
通讯作者：雷文太
学科门类：工学
一级学科：信息与通信工程
文献类型：J
是否译文：否
发表时间：2021-05-26
收录刊物：SCI、EI