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.

翻译：现有的图像超分辨率技术由于训练数据与实际场景之间存在显著差异，往往难以在复杂的真实世界环境中有效泛化。为解决这一挑战，先前研究或通过手动模拟复杂的物理退化过程，或采用基于学习的技术，但这些方法仍无法同时生成大规模、真实且多样化的数据。本文提出了一种新颖的无监督学习数据生成框架——真实解耦数据生成器，专为真实世界超分辨率任务设计。我们精心设计了内容与退化特征提取策略，并将其整合到一种新颖的内容-退化解耦扩散模型中，从而能够从未配对的真实低分辨率与高分辨率图像中生成逼真的低分辨率图像。大量实验表明，RealDGen在生成大规模、高质量且反映真实世界退化特征的配对数据方面表现卓越，显著提升了主流超分辨率模型在各类真实世界基准测试中的性能。