Images captured in degraded weather conditions often suffer from bad visibility. Pre-existing haze removal methods, the ones that are effective are computationally complex too. In common de-hazing approaches, estimation of atmospheric light is not achieved properly as a consequence, haze is not removed significantly from the sky region. In this paper, an efficient method of haze removal from a single image is introduced. To restore haze-free images comprising of both sky as well as nonsky regions, we developed a linear model to predict atmospheric light and estimated the transmission map using the dark channel prior followed by an application of a guided filter for quick refinement. Several experiments were conducted on a large variety of images, both reference and nonreference, where the proposed image de-hazing algorithm outperforms most of the prevalent algorithms in terms of perceptual visibility of the scene and computational efficiency. The proposed method has been empirically measured through quantitative and qualitative evaluations while retaining structure, edges, and improved color.

中文翻译：

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利用大气光估计技术增强朦胧图像

在恶劣天气条件下拍摄的图像通常会出现能见度差的问题。现有的除雾方法，有效的方法在计算上也很复杂。在常见的去雾方法中，大气光的估计没有正确实现，因此，天空区域的雾霾没有被显着去除。在本文中，介绍了一种从单个图像中去除雾度的有效方法。为了恢复包含天空和非天空区域的无雾图像，我们开发了一个线性模型来预测大气光，并使用暗通道先验估计透射图，然后应用引导滤波器进行快速细化。对大量图像进行了几次实验，包括参考和非参考，其中所提出的图像去雾算法在场景的感知可见性和计算效率方面优于大多数流行算法。所提出的方法已经通过定量和定性评估进行了经验测量，同时保留了结构、边缘和改进的颜色。