GPR图像的数据集构建及其DRDU-Net去噪算法

为了解决生成对抗网络（Generative Adversarial Network，GAN）在生成探地雷达（Ground Penetrating Radar，GPR）图像时存在训练不稳定的问题，提出利用带有梯度惩罚的Wasserstein距离生成对抗网络（WGAN-GP）生成GPR图像，并结合时域有限差分法和实地采集图像提出了一种构建GPR图像数据集的方法.相较于原始GAN与Wasserstein GAN等方法，WGAN-GP具有更好的稳定性，而且生成的GPR图像更接近真实图像.在此基础之上，将密集残差块和U-Net相结合提出了一种适合于GPR图像的密集残差去噪U-Net方法.该方法利用U-Net中编码-解码结构提高了GPR图像的去噪性能；同时，密集残差块的引入加强了GPR图像的特征复用，且使U-Net训练更加稳定.最后，利用仿真实验验证了所提去噪方法的性能，并与三维块匹配（BM3D）和U-Net方法进行了对比.结果表明：所提方法与BM3D以及U-Net去噪方法相比，具有更好的去噪效果.当σ等于20时，在模拟和实测数据上取平均值，其峰值信噪比分别提升了约6.5 dB和2.4 dB；结构相似性分别提升了约0.09和0.04.

Construction and DRDU-Net Based on Denoising Algorithm for GPR Image Dataset

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.