  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/.
