湖南大学学报（自然科学版）2024，Vol.51Issue(6)：20-28,9.DOI:10.16339/j.cnki.hdxbzkb.2024263
GPR图像的数据集构建及其DRDU-Net去噪算法
Construction and DRDU-Net Based on Denoising Algorithm for GPR Image Dataset
摘要
Abstract
To solve the problem of the training instability of the Generative Adversarial Network(GAN)in generating ground penetrating radar(GPR)images,the Wasserstein GAN with Gradient Penalty is used to generate the GPR images.Moreover,a new method for constructing the GPR dataset is proposed base on the Finite-difference time-domain method and the measured images.Compared with the original GAN and Wasserstein GAN methods,WGAN-GP has better stability and the generated GPR images are more similar to the actual images.On this basis,the Dense Residual Block(DRB)and the U-Net are combined to propose a Dense Residual Denoising U-Net(DRDU-Net)suitable for GPR images.It uses the coding and decoding process of U-Net to improve the denoising performance.In addition,the introduction of DRB enhances the feature reuse of GPR image and makes U-Net training more stable.The performance of the proposal is evaluated by simulation experiments and compared with the BM3D(Block-matching and 3D)and U-Net.The results show that our proposal has better denoising performance than BM3D and U-Net.When the variance is 20,the peak signal-to-noise ratio increases by about 6.5 dB and 2.4 dB and the structural similarity increases by 0.09 and 0.04,respectively.关键词
GPR数据集构建/GPR图像去噪/WGAN-GP/密集残差块Key words
GPR data set construction/GPR image denoising/WGAN-GP/dense residual block分类
信息技术与安全科学引用本文复制引用
王惠琴,高大庆,何永强,刘宾灿,王莹,曹明华..GPR图像的数据集构建及其DRDU-Net去噪算法[J].湖南大学学报（自然科学版）,2024,51(6):20-28,9.基金项目
国家自然科学基金资助项目(61861026,62261033,62265010),National Natural Science Foundation of China(61861026,62261033,62265010) （61861026,62261033,62265010）
甘肃省重点研发计划资助项目(23YFFA0060),The Key Research and Development Program of Gansu Province(23YFFA0060) （23YFFA0060）
