When Capture Is Not Enough: Synthetic Data for Real-World Arbitrary-Scale Image Super-Resolution

Achieving high-quality arbitrary-scale super-resolution (ASSR) results is crucial for real-world multimedia applications. However, existing real-world SR datasets are primarily constructed through multi-camera synchronization with post-alignment, beam splitters for direct paired capture, or zoom lenses for sequential imaging. Yet these approaches either fail to provide fine-grained scaling factors required for arbitrary SR or struggle to preserve degradation consistency in dynamic scenes due to temporal misalignment. Therefore, these methods are not suited for capturing real-world datasets for arbitrary-scale super-resolution. Since capturing genuine arbitrary-scale datasets is infeasible, we construct a real-world SR dataset with truly arbitrary-scales through a degradation-aware synthesis approach. Specifically, we extract degradation characteristics from real low-resolution (LR) images and employ them to synthesize LR images at arbitrary scales, ensuring that synthesized data faithfully reflects real-world degradation patterns. We further propose the DP-ArbSR, a Dual-Prior Arbitrary-Scale Super-Resolution Network, which explicitly incorporates both degradation priors and texture priors to enhance feature representation and local feature fusion. Extensive experiments demonstrate that our synthesized dataset enables models to generalize robustly to real-world degradations, and DP-ArbSR achieves competitive performance. The dataset and the implementation code used in this work will be released.