用流匹配生成真实世界模糊图像，解决超分辨率训练数据难题
Continuous Degradation Modeling via Latent Flow Matching for Real-World Super-Resolution
- 通过潜在退化空间的流匹配，从单张高清图合成带真实伪影的低清图
- 合成图像在未见退化级别下仍保持真实感，支持任意尺度超分训练
- 适合需要真实退化模拟的图像重建、超分辨率研究者使用
尽管基于深度学习的超分辨率（SR）方法在双三次下采样等合成退化场景中表现优异，但在包含噪声、模糊和压缩伪影等复杂非线性退化的现实图像上表现不佳。现有方法依赖费力构建的真实低分辨率（LR）与高分辨率（HR）图像对，通常仅限于特定下采样因子。为此，本文提出一种新框架，利用潜空间流匹配技术，从单张高清图合成逼真的低清图像。该方法可生成具有真实伪影且退化程度未见的低清图像，从而构建大规模真实世界超分辨率训练数据集。大量定量与定性评估表明，合成的低清图像能准确复现真实退化特征。同时，基于这些数据训练的传统及任意尺度超分模型均显著提升重建质量。
原文摘要 · Abstract (English)
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.
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