Unsupervised image blind super resolution via real degradation feature learning

Abstract

In recent years, many methods for image super-resolution (SR) have relied on pairs of low-resolution (LR) and high-resolution (HR) images for training, where the degradation process is predefined by bicubic downsampling. While such approaches perform well in standard benchmark tests, they often fail to accurately replicate the complexity of real-world image degradation. To address this challenge, researchers have proposed the use of unpaired image training to implicitly model the degradation process. However, there is a significant domain gap between the real-world LR and the synthetic LR images from HR, which severely degrades the SR performance. A novel unsupervised image-blind super-resolution method that exploits degradation feature-based learning for real-image super-resolution reconstruction (RDFL) is proposed. Their approach learns the degradation process from HR to LR using a generative adversarial network (GAN) and constrains the data distribution of the synthetic LR with real degraded images. The authors then encode the degraded features into a Transformer-based SR network for image super-resolution reconstruction through degradation representation learning. Extensive experiments on both synthetic and real datasets demonstrate the effectiveness and superiority of the RDFL method, which achieves visually pleasing reconstruction results.