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Day and Night-Time Dehazing by Local Airlight Estimation.
IEEE Transactions on Image Processing ( IF 10.6 ) Pub Date : 2020-04-23 , DOI: 10.1109/tip.2020.2988203
Cosmin Ancuti , Codruta O. Ancuti , Christophe De Vleeschouwer , Alan C. Bovik

We introduce an effective fusion-based technique to enhance both day-time and night-time hazy scenes. When inverting the Koschmieder light transmission model, and by contrast with the common implementation of the popular dark-channel DehazeHeCVPR2009, we estimate the airlight on image patches and not on the entire image. Local airlight estimation is adopted because, under night-time conditions, the lighting generally arises from multiple localized artificial sources, and is thus intrinsically non-uniform. Selecting the sizes of the patches is, however, non-trivial. Small patches are desirable to achieve fine spatial adaptation to the atmospheric light, but large patches help improve the airlight estimation accuracy by increasing the possibility of capturing pixels with airlight appearance (due to severe haze). For this reason, multiple patch sizes are considered to generate several images, that are then merged together. The discrete Laplacian of the original image is provided as an additional input to the fusion process to reduce the glowing effect and to emphasize the finest image details. Similarly, for day-time scenes we apply the same principle but use a larger patch size. For each input, a set of weight maps are derived so as to assign higher weights to regions of high contrast, high saliency and small saturation. Finally the derived inputs and the normalized weight maps are blended in a multi-scale fashion using a Laplacian pyramid decomposition. Extensive experimental results demonstrate the effectiveness of our approach as compared with recent techniques, both in terms of computational efficiency and the quality of the outputs.

中文翻译:

通过本地航空估计进行白天和晚上的除雾。

我们引入了一种有效的基于融合的技术来增强白天和夜间的朦胧场景。当反转Koschmieder透光模型时,与流行的暗通道DehazeHeCVPR2009的常见实现方式形成对比,我们估计图像斑块上而不是整个图像上的光线。之所以采用局部空中照明估计,是因为在夜间条件下,照明通常来自多个本地化的人工光源,因此本质上是不均匀的。但是,选择补丁的大小并非易事。需要小补丁以实现对大气光的精细空间适应,但是大补丁通过增加捕获具有空中外观的像素的可能性(由于严重的雾度)来帮助改善空中估计的准确性。为此原因,多个补丁大小被认为可以生成多个图像,然后将它们合并在一起。提供原始图像的离散拉普拉斯算子作为融合过程的附加输入,以减少发光效果并强调最精细的图像细节。同样,对于白天场景,我们采用相同的原理,但使用更大的色块大小。对于每个输入,导出一组权重图,以便将较高的权重分配给高对比度,高显着性和低饱和度的区域。最后,使用拉普拉斯金字塔分解以多尺度方式将导出的输入和归一化的权重图进行混合。大量的实验结果证明,与最新技术相比,我们的方法在计算效率和输出质量方面均有效。
更新日期:2020-04-23
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