  Existing image super-resolution (SR) techniques often fail to generalize
effectively in complex real-world settings due to the significant divergence
between training data and practical scenarios. To address this challenge,
previous efforts have either manually simulated intricate physical-based
degradations or utilized learning-based techniques, yet these approaches remain
inadequate for producing large-scale, realistic, and diverse data
simultaneously. In this paper, we introduce a novel Realistic Decoupled Data
Generator (RealDGen), an unsupervised learning data generation framework
designed for real-world super-resolution. We meticulously develop content and
degradation extraction strategies, which are integrated into a novel
content-degradation decoupled diffusion model to create realistic
low-resolution images from unpaired real LR and HR images. Extensive
experiments demonstrate that RealDGen excels in generating large-scale,
high-quality paired data that mirrors real-world degradations, significantly
advancing the performance of popular SR models on various real-world
benchmarks.
