我们提出了一种简单但是有效的图像先验规律——暗原色先验来为单一输入图像去雾。暗原色先验来自对户外无雾图像数据库的统计规律，它基于经观察得到的这么一个关键事实——绝大多数的户外无雾图像的每个局部区域都存在某些至少一个颜色通道的强度值很低的像素。利用这个先验建立的去雾模型，我们可直接估算雾的浓度并且复原得到高质量的去除雾干扰的图像。对户外各种不同的带雾图像的处理结果表明了 dark channel prior的巨大作用。同时，作为去雾过程中的副产品，我们还可获得该图像高质量的深度图。-we propose a simple but effective

image prior- dark channel prior to remove haze　a single

input image. The dark channel prior is a kind of statistics

of the haze-free outdoor images. It is based on a

key observation- most local patches in haze-free outdoor

images contain some pixels which have very low intensities

in at least one color channel. Using this prior with the haze

imaging model, we can directly estimate the thickness of the

haze and recover a high quality haze-free image. Results on

a variety of outdoor haze images demonstrate the power of

the proposed prior. Moreover, a high quality depth map can

also be obtained as a by-product of haze removal.