Improving Image de-raining Models using Reference-guided Transformers
Zihao Ye, Jaehoon Cho, and Changjae Oh
IEEE International Conference on Image Processing, 2024
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 meth- ods 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 vali- date our method on three datasets and show that our module can improve the performance of existing prior-based, CNN- based, and transformer-based approaches.