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README.md
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@@ -11,7 +11,7 @@ Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration
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<a rel="AAA" href="https://henlyta.github.io/ClearNight/mainpage.html">Page</a> |
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<a rel="AAA" href="https://huggingface.co/datasets/YuetongLiu/AllWeatherNight">Dataset</a>
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<
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We observe that uneven lighting conditions in real-world nighttime scenes often interact with weather degradations.
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To synthesize more realistic nighttime images with adverse weather conditions, we introduce an illumination-aware degradation generation approach.
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<img src="https://github.com/henlyta/ClearNight/blob/main/static/image/dataset2.png?raw=true">
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Dataset Statistics
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We synthesize 8,000 nighttime images for model training, encompassing both multi-degradation and single-degradation scenarios with various degradation scales, directions, patterns and intensities.
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The test dataset consists of two parts: a synthetic subset and a real subset, each containing 1,000 images.
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The 1,000 collected real-world images are categorized into 4 different degradation types and serve as the real subset for assessing models in real-world scenarios.
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Intended Use
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Our AllWeatherNight dataset is released under the BSD 3-Clause License, a permissive open-source license that grants users the freedom to use, copy, modify, and distribute the dataset, whether in its original form or as part of derivative works. The license is employed on generated degraded images and labels. The ground-truths from BDD100K and Exdark adhere to the BSD 3-Clause License.
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<
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If you find our work is helpful to your research, please cite the papers as follows:
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<div>
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<pre>
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@misc{ClearNight,
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title={Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration},
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author={Yuetong Liu and Yunqiu Xu and Yang Wei and Xiuli Bi and Bin Xiao},
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@@ -46,5 +48,7 @@ If you find our work is helpful to your research, please cite the papers as foll
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2505.16479},
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}
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</pre>
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</div>
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<a rel="AAA" href="https://henlyta.github.io/ClearNight/mainpage.html">Page</a> |
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<a rel="AAA" href="https://huggingface.co/datasets/YuetongLiu/AllWeatherNight">Dataset</a>
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<h2>AllWeatherNight</h2>
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We observe that uneven lighting conditions in real-world nighttime scenes often interact with weather degradations.
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To synthesize more realistic nighttime images with adverse weather conditions, we introduce an illumination-aware degradation generation approach.
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<img src="https://github.com/henlyta/ClearNight/blob/main/static/image/dataset2.png?raw=true">
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<h2>Dataset Statistics</h2>
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We synthesize 8,000 nighttime images for model training, encompassing both multi-degradation and single-degradation scenarios with various degradation scales, directions, patterns and intensities.
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The test dataset consists of two parts: a synthetic subset and a real subset, each containing 1,000 images.
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The 1,000 collected real-world images are categorized into 4 different degradation types and serve as the real subset for assessing models in real-world scenarios.
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<h2>Intended Use</h2>
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Our AllWeatherNight dataset is released under the BSD 3-Clause License, a permissive open-source license that grants users the freedom to use, copy, modify, and distribute the dataset, whether in its original form or as part of derivative works. The license is employed on generated degraded images and labels. The ground-truths from BDD100K and Exdark adhere to the BSD 3-Clause License.
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<h2>Citation</h2>
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If you find our work is helpful to your research, please cite the papers as follows:
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<div>
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<pre>
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<code>
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::before
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@misc{ClearNight,
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title={Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration},
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author={Yuetong Liu and Yunqiu Xu and Yang Wei and Xiuli Bi and Bin Xiao},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2505.16479},
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}
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::after
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</code>
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</pre>
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</div>
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