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（SR）技术在实际复杂场景中往往难以有效泛化，其根本原因在于训练数据与实际应用场景之间存在显著差异。为解决这一挑战，先前的研究要么手动模拟复杂的基于物理的退化过程，要么采用基于学习的技术，但这些方法仍难以同时生成大规模、真实且多样化的数据。本文提出了一种新型的真实解耦数据生成器（RealDGen），这是一种专为真实世界超分辨率设计的无监督学习数据生成框架。我们精心设计了内容与退化提取策略，并将其整合到新颖的内容-退化解耦扩散模型中，从而从非配对的真实低分辨率（LR）和高分辨率（HR）图像中生成真实的低分辨率图像。大量实验表明，RealDGen能够高效生成大规模、高质量且反映真实退化的配对数据，显著提升了主流超分辨率模型在多种真实世界基准测试上的性能。