GDSSR: Toward Real-World Ultra-High-Resolution Image Super-Resolution

Abstract

Although single image super-resolution (SR) has achieved great success, super-resolving the real-world Ultra-High-Resolution (UHR) image remains a challenging issue. Confronted with UHR images, most existing SR methods resort to patch-splitting so that the interconnections among the cropped patches are not attracted reasonable attention during the training and inference procedure. Rather than considering global image degradation levels and types, previous methods only focus on local degradation and unavoidably lead to inter-patch inconsistency, like blocking artifacts in the UHR image. To address this issue, we propose a real-world super-resolution framework to integrate the restoration of different patches through a Global Degradation Supervision Super-Resolution (GDSSR) method. Specifically, a lightweight Global Degradation Extractor is used for extracting global degradation features, which can facilitate restoring better local patches independently and enforce inter-patch consistency. Additionally, a joint training method of local and global patches is proposed to exercise global supervision during the training process, which enhances the degradation estimation and restores more natural results. Experiments show that our GDSSR method achieves superior restoration performance on real-world and UHR image SR datasets.