Improving Image De-raining Using Reference-Guided Transformers
This work addresses the challenge of producing high-quality de-rained images for outdoor vision systems, but it is incremental as it refines existing methods rather than proposing a new standalone approach.
The paper tackles the problem of improving image de-raining by introducing a reference-guided transformer module that uses a clean reference image to enhance results from existing methods, achieving performance gains across three datasets.
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.