Dehazing plays an important role in promoting the performance of outdoor computer vision systems. However, existing dehazing methods are targeted to daytime haze scenes, and are not able to improve visual effects for nighttime hazy images due to the unpredictable factors at night. In this paper, an effective single image dehazing framework based on image decomposition is presented for nighttime hazy images. First, the input single nighttime image is separated into the glow-shaped image and the glow-free nighttime hazy image using its relative smoothness constraint. Then, a novel structure-texture-noise decomposition model based on the exponentiated mean local variance is devised to split the nighttime hazy image into a structure layer, a texture layer and a noise layer, in which the structure layer and the texture layer are dehazed based on the maximum reflectance prior and the dark channel prior and enhanced in the gradient domain respectively. Finally, the dehazed structure layer and the enhanced texture layer are fused to produce a dehazed result. Experiments demonstrate that the proposed approach outperforms several state-of-the-art dehazing techniques for nighttime hazy scenes, especially in terms of noise suppression. Besides, the proposed algorithm is also capable of handling daytime hazy images and low-light degraded images.

除雾在提高室外计算机视觉系统的性能方面起着重要作用。然而，由于夜间的不可预测因素，现有的除雾方法仅针对白天的雾霾场景，并且无法改善夜间雾霾图像的视觉效果。本文提出了一种有效的基于图像分解的夜间雾图像去雾框架。首先，使用输入的单个夜间图像使用其相对平滑度约束将其分为辉光形图像和无辉光夜间模糊图像。然后，设计了一种基于指数平均局部方差的新型结构-纹理-噪声分解模型，将夜间模糊图像分为结构层，纹理层和噪声层，其中结构层和纹理层分别根据先验的最大反射比和暗通道的先验进行消雾，并分别在梯度域进行增强。最后，将除雾的结构层和增强的纹理层融合以产生除雾的结果。实验表明，对于夜间朦胧的场景，该方法优于几种最新的除雾技术，尤其是在噪声抑制方面。此外，所提出的算法还能够处理白天的模糊图像和弱光退化图像。实验表明，对于夜间朦胧的场景，该方法优于几种最新的除雾技术，尤其是在噪声抑制方面。此外，所提出的算法还能够处理白天的模糊图像和弱光退化图像。实验表明，对于夜间朦胧的场景，该方法优于几种最新的除雾技术，尤其是在噪声抑制方面。此外，所提出的算法还能够处理白天的模糊图像和弱光退化图像。