UAV-Rain1k: A Benchmark for Raindrop Removal from UAV Aerial Imagery
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929313074380800 |
|---|---|
| author | Chang, Wenhui Chen, Hongming He, Xin Chen, Xiang Shen, Liangduo |
| author_facet | Chang, Wenhui Chen, Hongming He, Xin Chen, Xiang Shen, Liangduo |
| contents | Raindrops adhering to the lens of UAVs can obstruct visibility of the background scene and degrade image quality. Despite recent progress in image deraining methods and datasets, there is a lack of focus on raindrop removal from UAV aerial imagery due to the unique challenges posed by varying angles and rapid movement during drone flight. To fill the gap in this research, we first construct a new benchmark dataset for removing raindrops from UAV images, called UAV-Rain1k. In this letter, we provide a dataset generation pipeline, which includes modeling raindrop shapes using Blender, collecting background images from various UAV angles, random sampling of rain masks and etc. Based on the proposed benchmark, we further present a comprehensive evaluation of existing representative image deraining algorithms, and reveal future research opportunities worth exploring. The proposed dataset is publicly available at https://github.com/cschenxiang/UAV-Rain1k. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05773 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UAV-Rain1k: A Benchmark for Raindrop Removal from UAV Aerial Imagery Chang, Wenhui Chen, Hongming He, Xin Chen, Xiang Shen, Liangduo Computer Vision and Pattern Recognition Raindrops adhering to the lens of UAVs can obstruct visibility of the background scene and degrade image quality. Despite recent progress in image deraining methods and datasets, there is a lack of focus on raindrop removal from UAV aerial imagery due to the unique challenges posed by varying angles and rapid movement during drone flight. To fill the gap in this research, we first construct a new benchmark dataset for removing raindrops from UAV images, called UAV-Rain1k. In this letter, we provide a dataset generation pipeline, which includes modeling raindrop shapes using Blender, collecting background images from various UAV angles, random sampling of rain masks and etc. Based on the proposed benchmark, we further present a comprehensive evaluation of existing representative image deraining algorithms, and reveal future research opportunities worth exploring. The proposed dataset is publicly available at https://github.com/cschenxiang/UAV-Rain1k. |
| title | UAV-Rain1k: A Benchmark for Raindrop Removal from UAV Aerial Imagery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.05773 |