Single image super-resolution using Wasserstein generative adversarial network with gradient penalty

Abstract

Due to its strong sample generating ability, Generative Adversarial Network (GAN) has been used to solve single image super-resolution (SISR) problem and obtains high perceptual quality super-resolution (SR) images. However, GAN suffers from the disadvantage of training instability, even fails to converge. In this paper, a new SISR method is proposed based on Wasserstein GAN, which is a training more stable GAN with Wasserstein metric. To further increase the SR performance and make the training process more easier and stable, two modifications are made on the original WGAN. First, a gradient penalty (GP) is adopted to replace weight clipping. Second, a new residual block with “pre-activation” of the weight layer is constructed in the generators of WGAN. Extensive experiments show that the proposed method yields superior SR performance than original GAN based SR methods and many other methods in accuracy and perceptual quality of ×4 magnification factor on four diverse testing datasets.