Denoising in Image-to-Image translation using Generative adversarial Network based on Wavelet transform

The image-to-image translation is one of the most challenging topics in artificial intelligence, which has recently made significant progress with the use of generative adversarial networks (GANs). However, existing methods often fail to translate the noise source to the target domain. This article presents the WTGAN network, which includes a new generator and a local and global discriminator to solve this problem. The generating network is designed based on wavelet transform and attention. Due to the fact that wavelet transforms are powerful tools for removing general noise from the image, They have been used in the structure of the generator. Also, attention, residual and skip-connections can provide deeper surface information between the source and target image and help to improve the generator performance. Experiments were performed on the Cityscapes dataset and PSNR, SSIM, and LPIPS criteria were used for evaluation. The results have shown that the model can well reduce the effects of noise at the source, well reserve structure, and achieve the desired quality.