中文摘要：

目的 针对已有图像去雾方法中存在的天空灰暗以及透射率分布与实际情况不一致导致的对比度增强不足等问题，以暗通道先验图像去雾方法为基础，提出结合天空检测与纹理滤波的图像去雾算法。方法 首先，设计了一个基于天空检测的大气光自适应估计策略，以天空区域亮度值较低的像素为依据估计大气光值，能够避免天空色彩失真，获得更明亮且干净的天空恢复结果；其次，对输入图像进行纹理平滑预处理以保持同一平面物体内的像素颜色一致性，并提出一个基于块偏移与导向滤波的透射率精确化计算策略，使透射率估值更符合深度信息的变化趋势，以提升无雾图像的对比度与色彩饱和度；最后，对复原结果进行联合双边滤波后处理，以降低噪声的影响。结果 本文算法得到的大气光估值更为合理，对于不符合暗通道先验的天空区域，能够取得更为自然的天空复原结果；本文算法得到的透射率的变化趋势与实际场景深度之间具有更高的一致性，对于符合暗通道先验的非天空区域，能够取得高对比度与高色彩饱和度的恢复结果。结论 本文算法在大气光与透射率的估值的准确性以及无雾图像的对比度与清晰度增强方面都得到了有效提升，具有较高的鲁棒性，适用于视频监控、交通监管和目标识别等户外获取图像的诸多应用领域。

英文摘要：

Objective Fog results in degradation of contrast and color saturation in the images shot outdoor. Therefore, the visibility of objects will decline, and details will be difficult to recognize. Thus, robust defogging techniques are valuable in the industrial fields driven by outdoor images or videos. Currently, the mainstream defogging methods are based on the foggy image degradation model, and two main problems that come from the estimation of atmospheric lights and transmissions remain. First, sky regions do not comply with the dark channel prior. The atmospheric light, which suffers from the interference of sunlight in the images with large sky area, is overestimated, resulting in gloomy sky regions in the fog-free images. Second, halo effects require being eliminated through a necessary transmission refinement process. However, the existing refinement methods will result in unreasonable transmissions with texture-like fluctuations inside the same planar objects, causing the inconsistency of variation trends between the transmission map and the depth information. Furthermore, these transmission fluctuations exert a negative influence on the contrast enhancement in the non-sky regions, which conform to the dark channel prior. To address the abovementioned problems, we combine sky detection with texture smoothing and propose a new image defogging algorithm. Method First, we design an adaptive atmospheric light estimation strategy based on sky detection, which can avoid gloomy restoration results of the sky regions, to address the overestimation of atmospheric lights in the images with sky regions. Initially, the foggy images are classified according to whether these images encompass sky regions. For the foggy images with sky, the pixels within the sky regions are sorted by their luminance values. The atmospheric light values are estimated according to the sky pixels with low luminance values. The problem of overestimated atmospheric light can be overcome through this strategy, resulting in bright and clean sky re