Image super‐resolution using conditional generative adversarial network

Abstract

Recently, extensive studies on a generative adversarial network (GAN) have made great progress in single image super‐resolution (SISR). However, there still exists a significant difference between the reconstructed high‐frequency and the real high‐frequency details. To address this issue, this study presents an SISR approach based on conditional GAN (SRCGAN). SRCGAN includes a generator network that generates super‐resolution (SR) images and a discriminator network that is trained to distinguish the SR images from ground‐truth high‐resolution (HR) ones. Specifically, the discriminator network uses the ground‐truth HR image as a conditional variable, which guides the network to distinguish the real images from the SR images, facilitating training a more stable generator model than GAN without this guidance. Furthermore, a residual‐learning module is introduced into the generator network to solve the issue of detail information loss in SR images. Finally, the network is trained in an end‐to‐end manner by optimizing a perceptual loss function. Extensive evaluations on four benchmark datasets including Set5, Set14, BSD100, and Urban100 demonstrate the superiority of the proposed SRCGAN over state‐of‐the‐art methods in terms of PSNR, SSIM, and visual effect.