Abstract
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.
Abstract (translated)
虽然基于深度学习的超分辨率（SR）方法在使用双三次降采样等合成退化场景下展示了令人印象深刻的结果，但在处理具有复杂、非线性退化因素如噪声、模糊和压缩伪影的真实世界图像时却常常表现不佳。为解决这一问题，最近的努力集中在精心编纂真实低分辨率（LR）和高分辨率（HR）图像对上，这些努力通常仅限于几个特定的降尺度因子。 为了应对上述挑战，我们的工作引入了一个新颖的框架，该框架可以从单个HR图像中合成具有实际退化效果的LR图像，通过利用潜在退化空间与流匹配技术。我们的方法可以生成在未知退化水平下具有现实瑕疵的低分辨率图像，从而有助于大规模真实世界SR训练数据集的创建。 全面的定量和定性评估验证了我们合成的LR图像能够准确再现现实生活中的退化情况。此外，使用我们的数据集进行训练的传统及任意尺度的超分辨模型在生成高分辨率结果方面均表现出显著改善。
URL
https://arxiv.org/abs/2602.04193
