While deep learning-based super-resolution (SR) methods have shown impressive outcomes with synthetic degradation scenarios such as bicubic downsampling, they frequently struggle to perform well on real-world images that feature complex, nonlinear degradations like noise, blur, and compression artifacts. Recent efforts to address this issue have involved the painstaking compilation of real low-resolution (LR) and high-resolution (HR) image pairs, usually limited to several specific downscaling factors. To address these challenges, our work introduces a novel framework capable of synthesizing authentic LR images from a single HR image by leveraging the latent degradation space with flow matching. Our approach generates LR images with realistic artifacts at unseen degradation levels, which facilitates the creation of large-scale, real-world SR training datasets. Comprehensive quantitative and qualitative assessments verify that our synthetic LR images accurately replicate real-world degradations. Furthermore, both traditional and arbitrary-scale SR models trained using our datasets consistently yield much better HR outcomes.

翻译：尽管基于深度学习的超分辨率方法在诸如双三次下采样等合成退化场景中已展现出令人印象深刻的结果，但它们在实际处理具有复杂非线性退化（如噪声、模糊和压缩伪影）的真实世界图像时，往往表现不佳。近期为解决此问题所做的努力，涉及费力地收集真实低分辨率与高分辨率图像对，这些配对通常仅限于几种特定的下采样因子。为应对这些挑战，我们的工作引入了一种新颖的框架，该框架能够通过利用隐式退化空间与流匹配技术，从单张高分辨率图像合成逼真的低分辨率图像。我们的方法能在未见过的退化级别上生成具有真实伪影的低分辨率图像，从而促进了大规模、真实世界超分辨率训练数据集的创建。全面的定量与定性评估证实，我们合成的低分辨率图像能准确复现真实世界的退化。此外，使用我们数据集训练的传统及任意尺度超分辨率模型，均能持续地产生更好的高分辨率重建结果。