一种基于暗通道先验的快速图像去雾算法

目前针对单幅图像去雾处理,暗通道先验算法具有较好的效果,但处理时间长,需要很大的储存资源与计算资源.本文在暗通道先验算法基础上提出一种快速去雾算法,首先用"边缘替代法"代替原算法中的抠图处理,显著降低了计算复杂度;然后针对明亮区域暗通道失效情况,提出了一种基于双阀值的明亮区域识别方法和透射率修正机制,提高了暗通道先验的适用范围;最后对去雾图像再采用非线性对比度拉伸进行增强处理,改善了去雾图像的视觉效果.实验表明:本文算法不仅极大地降低了计算复杂度,而且去雾能力优于原算法.

A Fast Image Daze Removal Algorithm Using Dark Channel Prior

While the dark channel prior algorithm works well for single image haze removal,it has to take long time to proces and require expensive computational overhead and huge memory resource.In this paper,we propose a fast daze removal algorithm based on dark channel prior knowledge.In the proposed approach,edge substitution method is developed to take place of the original soft-matting operator,which will substantially reduce the computational complexity.To deal with the invalid case of the dark channel,we exploit a scheme to identify bright area based on dual threshold and develop a way to correct transmission rate,which enables the dark channel prior to be more applicable.Additionally,enhanced processing is introduced by adopting nonlinear contrast stretching,thus improving the visual effect of defogging images.Several numerical experiments demonstrate that the proposed approach outperforms the original algorithm in daze removal effect at a much lower implementation cost.