基于天空分割的单幅交通标志图像去雾算法

针对现有去雾算法应用于交通标志图像时容易产生信息丢失、色彩失真等问题，导致去雾后图像质量较低，不能很好地满足交通标志识别系统（TSRS）的实际应用需求，提出一种基于天空分割的单幅交通标志图像去雾算法。根据大津算法结合图像灰度特征得到自适应阈值实现天空区域和非天空区域的准确分割；非天空区域采取改进的暗通道先验算法去雾，引入自适应中值滤波和快速双边滤波联合的方法优化透射率，天空区域则采取直方图均衡化算法去雾；通过融合得到无雾图像；引入高斯滤波对严重降质图像进行去雾后清晰化处理。实验结果表明，去雾后图像在峰值信噪比等多个客观评价指标上的综合表现优于其他几种去雾方法，所提算法在保证较低的时间复杂度的同时，能有效地保留图像信息，还原出清晰的真实图像，满足TSRS的实际应用需求。

Single Traffic Sign Image Defogging Algorithm Based on Sky Segmentation

For the issues of information loss and color distortion when the existing defogging algorithms are applied to traffic sign images, which lead to the lower quality of the defogged images and can not meet the practical application requirements of Traffic Sign Recognition System（TSRS）, a single traffic sign image defogging method based on sky segmentation is proposed. Firstly, according to Otsu algorithm and image gray features, an adaptive threshold is obtained to realize accurate segmentation of sky region and non-sky region. Non-sky area adopts improved DCP approach to defog, introducing adaptive median filter and fast bilateral filter to optimize transmittance, while sky area adopts histogram equalization algorithm to defog. A fog-free image is acquired by fusion. Finally, the Gaussian filter is introduced to clear the severely degraded image after defogging. Experimental results reveal that the comprehensive performance of defogged images in several objective evaluation indexes such as peak signal-to-noise ratio is better than other defogged methods. The proposed method can effectively preserve image information and restore clear images while ensuring lower time complexity, and meet the practical application requirements of TSRS.