Improving Image De-raining Using Reference-Guided Transformers
<|reference_start|>Improving Image De-raining Using Reference-Guided Transformers: 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.<|reference_end|>
@article{ye2024improving,
    title={Improving Image De-raining Using Reference-Guided Transformers},
    author={Zihao Ye, Jaehoon Cho, Changjae Oh},
    journal={arXiv preprint arXiv:2408.00258},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00258},
    primaryClass={cs.CV}
}