针对暗通道先验去雾算法中透射率估值不准确以及天空区域或大面积白色区域去雾后存在颜色失真等问题,提出了一种基于超像素分割和暗亮通道结合的单幅图像去雾方法。首先采用超像素方法对有雾图像进行分割,将得到的超像素块代替暗通道固定方形滤波窗口;其次,采用暗通道与亮通道先验理论结合的方法获取透射率,使透射率估值更准确;然后,在天空区域通过阈值分割结合亮通道先验理论确定大气光值,并利用融合梯度信息的引导滤波方法优化透射率;最后根据大气散射模型恢复无雾图像。实验结果表明,所提方法得到的透射率和大气光值的估值准确,取得了良好的去雾效果,在主观评价和客观评价方面均优于其他对比算法。

As for the problems such as the inaccuracy of transmission estimation and the color distortion of sky areas or large white area using dark channel prior dehazing, we propose a single image dehazing method based on superpixel segmentation combined with dark-bright channels. First, the superpixel method is used to segment the hazy image, and the obtained superpixel block replaces the fixed square filter window of the dark channel. Second, the prior method which combines dark and bright channels is used to obtain the atmospheric transmittance, and thus the transmittance estimation is more accurate. Thirdly, the atmospheric light value is determined by threshold segmentation combined with the bright channel prior theory in the sky region, and subsequently the transmittance is optimized by the guidance filter method with gradient information. Finally, the hazy image is restored to the dehazed image based on the atmospheric scattering model. The experimental results show that the transmittance and the atmospheric light value estimated by the proposed method are accurate, and a good dehazing effect can be obtained. The proposed method is superior to other comparison algorithms in subjective and objective evaluations.