Spatial Transformer Generative Adversarial Network for Robust Image Super-Resolution

Abstract

Recently, there have been significant advances in image super-resolution based on generative adversarial networks (GANs) to achieve breakthroughs in generating more images with high subjective quality. However, there are remaining challenges needs to be met, such as simultaneously recovering the finer texture details for large upscaling factors and mitigating the geometric transformation effects. In this paper, we propose a novel robust super-resolution GAN (i.e. namely RSR-GAN) which can simultaneously perform both the geometric transformation and recovering the finer texture details. Specifically, since the performance of the generator depends on the discreminator, we propose a novel discriminator design by incorporating the spatial transformer module with residual learning to improve the discrimination of fake and true images through removing the geometric noise, in order to enhance the super-resolution of geometric corrected images. Finally, to further improve the perceptual quality, we introduce an additional DCT loss term into the existing loss function. Extensive experiments, measured by both PSNR and SSIM measurements, show that our proposed method achieves a high level of robustness against a number of geometric transformations, including rotation, translation, a combination of rotation and scaling effects, and a cobmination of rotaion, transalation and scaling effects. Benchmarked by the existing state-of-the-arts SR methods, our proposed delivers superior performances on a wide range of datasets which are publicly available and widely adopted across research communities.