Bidirectional Multi-Scale Implicit Neural Representations for Image
  Deraining
Xiang Chen Jinshan Pan Jiangxin Dong
How to effectively explore multi-scale representations of rain streaks is
important for image deraining. In contrast to existing Transformer-based
methods that depend mostly on single-scale rain appearance, we develop an
end-to-end multi-scale Transformer that leverages the potentially useful
features in various scales to facilitate high-quality image reconstruction. To
better explore the common degradation representations from spatially-varying
rain streaks, we incorporate intra-scale implicit neural representations based
on pixel coordinates with the degraded inputs in a closed-loop design, enabling
the learned features to facilitate rain removal and improve the robustness of
the model in complex scenarios. To ensure richer collaborative representation
from different scales, we embed a simple yet effective inter-scale
bidirectional feedback operation into our multi-scale Transformer by performing
coarse-to-fine and fine-to-coarse information communication. Extensive
experiments demonstrate that our approach, named as NeRD-Rain, performs
favorably against the state-of-the-art ones on both synthetic and real-world
benchmark datasets. The source code and trained models are available at
https://github.com/cschenxiang/NeRD-Rain.