提出了一种基于暗通道图像质心偏移量的去雾算法。该算法对雾天图像暗通道进行了聚类分析,按场景划分图像并分析和计算了每个场景暗通道图像的质心偏移量,以用于场景的透射率修正。结合四叉树搜索算法,提出了基于景深阶跃图的大气光值估计方法,使估算大气光值的位置不受白色或平坦物体的影响而落到景深较大的区域。实验结果表明,所提算法能有效地恢复明亮区域的原本色调和细节信息,复原图像亮度适宜且颜色自然。在主观上,复原图像有较好的视觉效果;在客观上,所提算法的复原图像评价指标整体优于暗通道先验算法。

In this paper, we propose a dehazing algorithm based on the dark-channel image centroid offset. The algorithm clusters the dark channels of hazy images to divide these images into scenes. Further, it analyzes and calculates the centroid offset of the dark-channel image of each scene to correct the transmission rate of the scene. Combined with the quadtree search algorithm, an atmospheric light estimation method based on the depth of field step image is proposed, which enables the estimated position of atmospheric light to fall in a region with a large depth of field without being affected by white or flat objects. The experimental results reveal that the proposed algorithm can effectively restore the original hue of bright regions as well as the detail information. Moreover, the restored images have appropriate brightness and natural color. Subjectively, the restored images have relatively good visual effects. Objectively, the evaluation indexes of the restored images by the proposed algorithm are overall better than those by the dark-channel-prior algorithm.