基于视觉感知的快速雾天图像清晰度复原

针对雾霾恶劣天气状况下获取的图像视觉效果差，本文提出了一种基于视觉感知的快速雾天图像清晰度复原方法，测算大气光学物理模型的两个重要参量。首先采用阈值分割结合二叉树分割的方法拟合较为精准的大气光值，进而采用自适应各向异性型高斯滤波与色调调整方法优化透射率。与算法1,3,5,8]实验结果表明，本文算法的去雾效果图效果饱和清晰，能够保留清晰的边缘细节和较高的对比度，算法的处理效率高，能满足实际应用需求。

Fast fog image restoration based on visual perception

Under bad weather conditions, such as heavy haze, the visual effect of obtained images is poor. This paper proposes a fast restoration method of haze images based on visual perception, and measures two important parameters of atmospheric optical physical model. Firstly, in order to fit a relatively accurate atmospheric light value, this paper uses threshold segmentation combined with binary tree segmentation method, and then adopts adaptive Gaussian filtering and tone adjustment methods to optimize the transmittance. The experimental results show that In comparison with the algorithm 1,3,5,8], the effectiveness of the dehazing results of our algorithm is saturated and clear. Besides, it also can retain clear edge details ,high contrast, high processing efficiency and can meet the actual application requirements.