Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913793731198976 |
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| author | Fang, Chengyu He, Chunming Xiao, Fengyang Zhang, Yulun Tang, Longxiang Zhang, Yuelin Li, Kai Li, Xiu |
| author_facet | Fang, Chengyu He, Chunming Xiao, Fengyang Zhang, Yulun Tang, Longxiang Zhang, Yuelin Li, Kai Li, Xiu |
| contents | Real-world Image Dehazing (RID) aims to alleviate haze-induced degradation in real-world settings. This task remains challenging due to the complexities in accurately modeling real haze distributions and the scarcity of paired real-world data. To address these challenges, we first introduce a cooperative unfolding network that jointly models atmospheric scattering and image scenes, effectively integrating physical knowledge into deep networks to restore haze-contaminated details. Additionally, we propose the first RID-oriented iterative mean-teacher framework, termed the Coherence-based Label Generator, to generate high-quality pseudo labels for network training. Specifically, we provide an optimal label pool to store the best pseudo-labels during network training, leveraging both global and local coherence to select high-quality candidates and assign weights to prioritize haze-free regions. We verify the effectiveness of our method, with experiments demonstrating that it achieves state-of-the-art performance on RID tasks. Code will be available at https://github.com/cnyvfang/CORUN-Colabator. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07966 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network Fang, Chengyu He, Chunming Xiao, Fengyang Zhang, Yulun Tang, Longxiang Zhang, Yuelin Li, Kai Li, Xiu Computer Vision and Pattern Recognition Real-world Image Dehazing (RID) aims to alleviate haze-induced degradation in real-world settings. This task remains challenging due to the complexities in accurately modeling real haze distributions and the scarcity of paired real-world data. To address these challenges, we first introduce a cooperative unfolding network that jointly models atmospheric scattering and image scenes, effectively integrating physical knowledge into deep networks to restore haze-contaminated details. Additionally, we propose the first RID-oriented iterative mean-teacher framework, termed the Coherence-based Label Generator, to generate high-quality pseudo labels for network training. Specifically, we provide an optimal label pool to store the best pseudo-labels during network training, leveraging both global and local coherence to select high-quality candidates and assign weights to prioritize haze-free regions. We verify the effectiveness of our method, with experiments demonstrating that it achieves state-of-the-art performance on RID tasks. Code will be available at https://github.com/cnyvfang/CORUN-Colabator. |
| title | Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.07966 |