DWGAN: a dual Wasserstein-autoencoder-based generative adversarial network for image super-resolution

Abstract

Significant progress has been made with image super resolution using deep learning in recent years. However, existing methods aim at minimizing differences between pixels, which means they fail to consider the distribution difference while reconstructing images. To address this challenge, we propose DWGAN: a dual Wasserstein-Autoencoder based generative adversarial network. Our DWGAN successfully implemented the adversarial training process both in the latent space and the image space. In the latent space, the adversarial training process successfully models the latent distribution with Wasserstein Autoencoders and the adversarial training in the image space achieves better reconstruction performance. Besides, we also utilize tree-structure residual encoders to extract 2-dimensional latent features of the same size as low-resolution (LR) inputs, which enables pixel-wise matching. Experiments show that our DWGAN achieves better visual and quantitive results compared with other GAN-based models.