Guided Frequency Separation Network for Real-World Super-Resolution

Training image pairs are unavailable generally in real-world super-resolution. Although the LR images can be down-scaled from HR images, some real-world characteristics (such as artifacts or sensor noise) have been removed from the degraded images. Therefore, most of state-of-the-art super-resolved methods often fail in real-world scenes. In order to address aforementioned problem, we proposed an unsupervised super-resolved solution. The method can be divided into two stages: domain transformation and super-resolution. A color-guided domain mapping network was proposed to alleviate the color shift in domain transformation process. In particular, we proposed the Color Attention Residual Block (CARB) as the basic unit of the domain mapping network. The CARB which can dynamically regulate the parameters is driven by input data. Therefore, the domain mapping network can result in the powerful generalization performance. Moreover, we modified the discriminator of the super-resolution stage so that the network not only keeps the high frequency features, but also maintains the low frequency features. Finally, we constructed an EdgeLoss to improve the texture details. Experimental results show that our solution can achieve a competitive performance on NTIRE 2020 real-world super-resolution challenge.