  Image de-raining is a critical task in computer vision to improve visibility
and enhance the robustness of outdoor vision systems. While recent advances in
de-raining methods have achieved remarkable performance, the challenge remains
to produce high-quality and visually pleasing de-rained results. In this paper,
we present a reference-guided de-raining filter, a transformer network that
enhances de-raining results using a reference clean image as guidance. We
leverage the capabilities of the proposed module to further refine the images
de-rained by existing methods. We validate our method on three datasets and
show that our module can improve the performance of existing prior-based,
CNN-based, and transformer-based approaches.
