Abstract Scene visibility in outdoor images is often deteriorated by bad weather conditions such as snow, haze, and rain. Especially, degradation due to haze is typically observed in the form of faded color and low contrast of images. To overcome image degradation, dehazing algorithms based on the atmospheric scattering model employ transmission map estimation, which is related to the haze density across the depth of scenes. However, estimating the depth of the outdoor scene without additional information is challenging, and an erroneously estimated depth leads to a dehazed image of poor quality. In this paper, we propose a fusion-based dehazing algorithm that does not require direct estimation of the transmission map. The intermediate latent images are obtained by restoring the scene radiance with several globally constant transmission values based on the atmospheric scattering model. Using this approach, only the region where the particular transmission corresponds to the ground truth is dehazed successfully in each image. These images are merged into a haze-free result via the fusion algorithm, which selectively uses the information of the patches from the latent images. Experimental results show that the proposed algorithm effectively removes haze and outperforms the several dehazing methods based on quantitative and qualitative evaluations.

中文翻译：

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基于大气散射模型图像融合的单幅图像去雾

摘要 户外图像中的场景能见度经常因雪、雾、雨等恶劣天气条件而恶化。特别是，由于雾度导致的退化通常以褪色和图像对比度低的形式观察到。为了克服图像退化，基于大气散射模型的去雾算法采用透射图估计，这与场景深度的雾度密度有关。然而，在没有附加信息的情况下估计室外场景的深度是具有挑战性的，并且错误估计的深度会导致质量差的去雾图像。在本文中，我们提出了一种不需要直接估计透射图的基于融合的去雾算法。中间潜像是通过基于大气散射模型用几个全局恒定透射值恢复场景辐射获得的。使用这种方法，只有特定传输对应于地面实况的区域才能在每个图像中成功去雾。这些图像通过融合算法合并为无雾结果，该算法有选择地使用来自潜在图像的补丁信息。实验结果表明，该算法有效去除雾霾，优于基于定量和定性评价的几种去雾方法。这些图像通过融合算法合并为无雾结果，该算法有选择地使用来自潜在图像的补丁信息。实验结果表明，该算法有效去除雾霾，优于基于定量和定性评价的几种去雾方法。这些图像通过融合算法合并为无雾结果，该算法有选择地使用来自潜在图像的补丁信息。实验结果表明，该算法有效去除雾霾，优于基于定量和定性评价的几种去雾方法。