Single image dehazing is a difficult problem because of its ill-posed nature. Increasing attention has been paid recently as its high potential applications in many visual tasks. Although single image dehazing has made remarkable progress in recent years, they are mainly designed for haze removal in daytime. In nighttime, dehazing is more challenging where most daytime dehazing methods become invalid due to multiple scattering phenomena, and non-uniformly distributed dim ambient illumination. While a few approaches have been proposed for nighttime image dehazing, low ambient light is actually ignored. In this paper, we propose a novel unified nighttime hazy image enhancement framework to address the problems of both haze removal and illumination enhancement simultaneously. Specifically, both halo artifacts caused by multiple scattering and non-uniformly distributed ambient illumination existing in low-light hazy conditions are considered for the first time in our approach. More importantly, most current daytime dehazing methods can be effectively incorporated into nighttime dehazing task based on our framework. Firstly, we decompose the observed hazy image into a halo layer and a scene layer to remove the influence of multiple scattering. After that, we estimate the spatially varying ambient illumination based on the Retinex theory. We then employ the classic daytime dehazing methods to recover the scene radiance. Finally, we generate the dehazing result by combining the adjusted ambient illumination and the scene radiance. Compared with various daytime dehazing methods and the state-of-the-art nighttime dehazing methods, both quantitative and qualitative experimental results on both real-world and synthetic hazy image datasets demonstrate the superiority of our framework in terms of halo mitigation, visibility improvement and color preservation.

中文翻译：

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SIDE-同时消雾和增强夜间模糊图像的统一框架。

单图像去雾由于其不适性而成为一个难题。近来，由于其在许多视觉任务中的高潜力应用，已引起越来越多的关注。尽管近年来单图像除雾取得了显着进展，但它们主要设计用于白天的除雾。在夜间，由于多种散射现象和昏暗的环境照明不均匀分布，大多数白天的除雾方法变得无效，因此除雾更具挑战性。虽然已经提出了一些用于夜间图像去雾的方法，但实际上忽略了低环境光。在本文中，我们提出了一种新颖的统一夜间模糊图像增强框架，以同时解决雾度去除和照明增强的问题。特别，在我们的方法中，首次考虑了由多重散射和低光照朦胧条件下存在的不均匀分布的环境照明导致的光晕伪影。更重要的是，根据我们的框架，可以将大多数当前的白天除雾方法有效地整合到夜间除雾任务中。首先，我们将观察到的模糊图像分解为一个光晕层和一个场景层，以消除多重散射的影响。之后，我们根据Retinex理论估算空间变化的环境照明。然后，我们采用经典的白天除雾方法来恢复场景辐射。最后，我们通过组合调整后的环境照度和场景辐射度来生成除雾结果。与各种白天除雾方法和最新的夜间除雾方法相比，