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来源： moonapi
更新时间： 2023-12-05 最新数据时间： 自动更新
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数据集介绍： 单图像去叠是一个不适定问题，最近引起了重要关注。尽管在过去几年中，人们对去雾的兴趣显著增加，但由于缺乏真实的雾度和相应的无雾度参考图像对，去雾方法的验证在很大程度上仍然不令人满意。为了解决这一局限性，我们引入了一种新的去雾数据集稠密雾。《浓雾》以浓密均匀的朦胧场景为特征，包含33对真实的朦胧图像和各种室外场景的相应无霾图像。通过引入由专业雾霾机器生成的真实雾霾来记录雾霾场景。朦胧和无

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dense-haze简介

数据集介绍：


单图像去叠是一个不适定问题，最近引起了重要关注。尽管在过去几年中，人们对去雾的兴趣显著增加，但由于缺乏真实的雾度和相应的无雾度参考图像对，去雾方法的验证在很大程度上仍然不令人满意。为了解决这一局限性，我们引入了一种新的去雾数据集稠密雾。《浓雾》以浓密均匀的朦胧场景为特征，包含33对真实的朦胧图像和各种室外场景的相应无霾图像。通过引入由专业雾霾机器生成的真实雾霾来记录雾霾场景。朦胧和无朦胧的对应场景包含在相同照明参数下捕获的相同视觉内容。


 


引用：


@inproceedings{nh-haze_2020,

author = {codruta o. ancuti and cosmin ancuti and radu timofte},

title = {{nh-haze:} an image dehazing benchmark with non-homogeneous hazy and haze-free images},

booktitle =  {proceedings of the ieee conference on computer vision and pattern recognition workshops},

series = {ieee cvpr 2020},

year = {2020},

location = {washington, us},

}

 

@inproceedings{ntire_dehazing_2020,

author = {codruta o ancuti and cosmin ancuti and florin-alexandru vasluianu and radu timofte and others},

title = {{ntire} 2020 challenge on nonhomogeneous dehazing},

booktitle =  {proceedings of the ieee conference on computer vision and pattern recognition workshops},

series = {ieee cvpr 2020},

year = {2020},

location = {washington, us},

}
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collection of hazy/non-hazy image pairs
many dehazing methods have proven to be effective in removing haze out of the hazy image, but few of them are adaptive in handling the dense haze. in this paper, based on the angle of polarization (aop) distribution analysis we propose a kind of polarimetric dehazing method, which is verified to be …
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密雾以 密集、均匀的朦胧场景为特征，包含 33对真实的朦胧和相应的无雾霾图像。为了产生模糊的场景，使用了一个专业的雾霾机，模拟与高保真的真实雾霾。为了保持光照条件，所有的室外场景都是静态的，并在阴天、早晨或日落时被记录下来。基本上，密集-雾霾 扩展了o-haze [31]数据集，该数据集最近被用于第一个有组织的[32]的单一图像脱雾挑战。与只包含 轻雾场景的o-haze相比，密集雾层更具挑战性，因为所有记录的场景都包含更密集、更均匀的雾层层（见图1）。引入密集的雾霾数据集将显著推动最先进的单图像脱雾方法，使其对真实和各种密集的雾霾场景更加鲁棒。

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