基于黑色通道的图像快速去雾优化算法

Optimized Fast Dehazing Method Based on Dark Channel Prior

黑色通道优先算法在处理单幅户外场景图像去雾方面取得了一定的效果,但是该方法需要消耗大量的存储和计算资源,另外对天空区域的处理不够准确.本文仍然基于黑色通道先验知识,对图像边缘和非边缘分别采用不同的模板处理得到透射图,并通过分割天空区域或者雾最浓区域求得准确的大气光值,利用该方法可以恢复出和原方法基本一致甚至更准确的结果,并且可以大大减少消耗,提升计算速度.

The dark channel prior algorithm is effective to some extent when working on some single outdoor scene hazing images. However, it has to expend large amounts of memory storage and computational time. Besides, the result of the sky region is not accurate enough. In this paper, we estimate the transmission map through processing the edges and non-edges respectively with different neighborhoods and obtain more accurate almosphefic light value by departing the sky part or the most hazed region of the image. This method can get a similar or even a better dehazing result than the classical haze removal methods, and moreover take less memory consumption and get much faster computational speed.