Not Just Streaks: Towards Ground Truth for Single Image Deraining
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909270891560960 |
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| author | Ba, Yunhao Zhang, Howard Yang, Ethan Suzuki, Akira Pfahnl, Arnold Chandrappa, Chethan Chinder de Melo, Celso You, Suya Soatto, Stefano Wong, Alex Kadambi, Achuta |
| author_facet | Ba, Yunhao Zhang, Howard Yang, Ethan Suzuki, Akira Pfahnl, Arnold Chandrappa, Chethan Chinder de Melo, Celso You, Suya Soatto, Stefano Wong, Alex Kadambi, Achuta |
| contents | We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image. As there exists no real-world dataset for deraining, current state-of-the-art methods rely on synthetic data and thus are limited by the sim2real domain gap; moreover, rigorous evaluation remains a challenge due to the absence of a real paired dataset. We fill this gap by collecting a real paired deraining dataset through meticulous control of non-rain variations. Our dataset enables paired training and quantitative evaluation for diverse real-world rain phenomena (e.g. rain streaks and rain accumulation). To learn a representation robust to rain phenomena, we propose a deep neural network that reconstructs the underlying scene by minimizing a rain-robust loss between rainy and clean images. Extensive experiments demonstrate that our model outperforms the state-of-the-art deraining methods on real rainy images under various conditions. Project website: https://visual.ee.ucla.edu/gt_rain.htm/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_10779 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Not Just Streaks: Towards Ground Truth for Single Image Deraining Ba, Yunhao Zhang, Howard Yang, Ethan Suzuki, Akira Pfahnl, Arnold Chandrappa, Chethan Chinder de Melo, Celso You, Suya Soatto, Stefano Wong, Alex Kadambi, Achuta Computer Vision and Pattern Recognition We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image. As there exists no real-world dataset for deraining, current state-of-the-art methods rely on synthetic data and thus are limited by the sim2real domain gap; moreover, rigorous evaluation remains a challenge due to the absence of a real paired dataset. We fill this gap by collecting a real paired deraining dataset through meticulous control of non-rain variations. Our dataset enables paired training and quantitative evaluation for diverse real-world rain phenomena (e.g. rain streaks and rain accumulation). To learn a representation robust to rain phenomena, we propose a deep neural network that reconstructs the underlying scene by minimizing a rain-robust loss between rainy and clean images. Extensive experiments demonstrate that our model outperforms the state-of-the-art deraining methods on real rainy images under various conditions. Project website: https://visual.ee.ucla.edu/gt_rain.htm/. |
| title | Not Just Streaks: Towards Ground Truth for Single Image Deraining |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2206.10779 |