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.