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@@ -7,9 +7,19 @@ Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration
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  </h1>
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  <a rel="AAA" href="https://henlyta.github.io/ClearNight/mainpage.html">Paper</a> |
 
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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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  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.
@@ -17,6 +27,8 @@ The test dataset consists of two parts: a synthetic subset and a real subset, ea
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  The synthetic subset evaluates models across 7 dimensions, covering synthetic images with both multiple and single degradations.
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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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  </h1>
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  <a rel="AAA" href="https://henlyta.github.io/ClearNight/mainpage.html">Paper</a> |
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+ <a rel="AAA" href="https://github.com/henlyta/ClearNight">Github</a> |
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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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+ <h3>AllWeatherNight:</h3>
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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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+ We show four different synthetic image variants of nighttime scenes.
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+ Weather Only and Flare Only denote synthesis with illumination-aware weather degradation and flare, respectively. Ours involves synthesis with both types of degradations.
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+ <img src="https://github.com/henlyta/ClearNight/blob/main/static/image/dataset2.png">
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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 synthetic subset evaluates models across 7 dimensions, covering synthetic images with both multiple and single degradations.
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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.