Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified
  Removal of Raindrops and Rain Streaks
Sixiang Chen Tian Ye Jinbin Bai Erkang Chen Jun Shi Lei Zhu
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.