Improving Image De-raining Using Reference-Guided Transformers
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916342231203840 |
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| author | Ye, Zihao Cho, Jaehoon Oh, Changjae |
| author_facet | Ye, Zihao Cho, Jaehoon Oh, Changjae |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00258 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Image De-raining Using Reference-Guided Transformers Ye, Zihao Cho, Jaehoon Oh, Changjae Computer Vision and Pattern Recognition 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. |
| title | Improving Image De-raining Using Reference-Guided Transformers |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.00258 |