Haze removal from nighttime images is more difficult compared with daytime image dehazing due to the uneven illumination, low contrast and severe color distortion. In this paper, following the approaches based on Dark channel prior, we propose a simple yet effective approach using Retinex theory and Taylor series expansion for nighttime image dehazing, referred to as ‘RDT’. Existing nighttime image dehazing methods do not handle color shift and glow removal very well. In order to address these issues, we first propose to decompose the atmospheric light image from the input image based on the Retinex theory. Taylor series expansion is then introduced for the first time to accurately estimate the pointwise transmission map. Finally, during the following processes of image fusion and color transfer, the atmospheric light image and potential haze-free image are adopted to obtain the final haze-free image. The experimental results on benchmark nighttime haze images demonstrate the superior performance of our proposed RDT dehazing method over the state-of-the-art methods.

与夜间除雾相比，由于夜间照明不均匀，对比度低和严重的色彩失真，从夜间除雾比在夜间除雾更为困难。在本文中，遵循基于暗通道先验的方法，我们提出了一种使用Retinex理论和泰勒级数展开进行夜间图像除雾的简单而有效的方法，称为“ RDT”。现有的夜间图像去雾方法不能很好地处理色移和辉光去除。为了解决这些问题，我们首先建议根据Retinex理论从输入图像中分解大气光图像。然后，首次引入泰勒级数展开，以准确估计逐点透射图。最后，在以下图像融合和颜色转移过程中，大气光图像和潜在的无雾图像被采用以获得最终的无雾图像。在基准夜间雾度图像上的实验结果表明，我们提出的RDT除雾方法优于最先进的方法。