The capability of video super-resolution (VSR) to synthesize high-resolution (HR) video from ideal datasets has been demonstrated in many works. However, applying the VSR model to real-world video with unknown and complex degradation remains a challenging task. First, existing degradation metrics in most VSR methods are not able to effectively simulate real-world noise and blur. On the contrary, simple combinations of classical degradation are used for real-world noise modeling, which led to the VSR model often being violated by out-of-distribution noise. Second, many SR models focus on noise simulation and transfer. Nevertheless, the sampled noise is monotonous and limited. To address the aforementioned problems, we propose a Negatives augmentation strategy for generalized noise modeling in Video Super-Resolution (NegVSR) task. Specifically, we first propose sequential noise generation toward real-world data to extract practical noise sequences. Then, the degeneration domain is widely expanded by negative augmentation to build up various yet challenging real-world noise sets. We further propose the augmented negative guidance loss to learn robust features among augmented negatives effectively. Extensive experiments on real-world datasets (e.g., VideoLQ and FLIR) show that our method outperforms state-of-the-art methods with clear margins, especially in visual quality. Project page is available at: https://negvsr.github.io/.

翻译：视频超分辨率（VSR）从理想数据集中合成高分辨率（HR）视频的能力已在多项工作中得到验证。然而，将VSR模型应用于存在未知复杂退化的真实视频仍是一项具有挑战性的任务。首先，现有VSR方法中的多数退化指标无法有效模拟真实噪声与模糊。相反，现有方法仅采用经典退化的简单组合进行真实噪声建模，导致VSR模型常因分布外噪声而产生偏差。其次，许多超分辨率模型聚焦于噪声模拟与迁移，但采样的噪声具有单调性和局限性。针对上述问题，我们提出了一种面向视频超分辨率任务中通用噪声建模的负样本增强策略（NegVSR）。具体而言，我们首先提出针对真实数据的序列化噪声生成方法以提取实际噪声序列，继而通过负样本增强大幅扩展退化域以构建多样且具有挑战性的真实噪声集。此外，我们进一步提出增强型负样本引导损失，以在增强负样本中有效学习鲁棒特征。在VideoLQ和FLIR等真实数据集上的大量实验表明，本方法在视觉质量等方面均以显著优势超越现有最优方法。项目页面请访问：https://negvsr.github.io/。