In the real world, image degradations caused by rain often exhibit a
combination of rain streaks and raindrops, thereby increasing the challenges of
recovering the underlying clean image. Note that the rain streaks and raindrops
have diverse shapes, sizes, and locations in the captured image, and thus
modeling the correlation relationship between irregular degradations caused by
rain artifacts is a necessary prerequisite for image deraining. This paper aims
to present an efficient and flexible mechanism to learn and model degradation
relationships in a global view, thereby achieving a unified removal of
intricate rain scenes. To do so, we propose a Sparse Sampling Transformer based
on Uncertainty-Driven Ranking, dubbed UDR-S2Former. Compared to previous
methods, our UDR-S2Former has three merits. First, it can adaptively sample
relevant image degradation information to model underlying degradation
relationships. Second, explicit application of the uncertainty-driven ranking
strategy can facilitate the network to attend to degradation features and
understand the reconstruction process. Finally, experimental results show that
our UDR-S2Former clearly outperforms state-of-the-art methods for all
benchmarks.

提出了一种基于不确定性驱动排序的稀疏采样变换器，称为 UDR-S2Former，用于学习和建模全局图像退化关系，从而实现复杂雨天场景的统一去雨。与先前方法相比，UDR-S2Former 具有三个优点：可自适应地采样相关图像退化信息以建模底层退化关系；明确应用基于不确定性驱动的排序策略有助于网络关注退化特征并理解重建过程；实验结果表明，UDR-S2Former 在所有基准测试中明显优于现有方法。