Most dehazing methods fail to estimate satisfactory transmission simultaneously in both normal and bright regions. To estimate more accurate transmission for these two kinds of regions, we propose a transmission fusion strategy based on automatic image matting for image dehazing. We first extract the mean and variance of a local patch around each pixel, and propose a binary classification method with the mean and variance of each patch to coarsely segment an input image into a binary map of normal and bright regions. Then we smooth and quantize the binary map to automatically generate a trimap of ternary values. Thus we can avoid the difficulty in manually labeling trimaps. Both the image and the trimap are input into a Bayesian matting method for soft segmentation of normal and bright regions to produce an alpha map. The dark channel prior (DCP) is adopted to extract a transmission map for normal regions, while an improved atmospheric veil correction (AVC) method is proposed to generate another transmission map for bright regions. Finally, we propose to use the alpha map to fuzzily fuse the two transmission maps for final image dehazing. Experimental results show that our method significantly outperforms existing methods.

大多数除雾方法无法同时估计正常区域和明亮区域的令人满意的透射率。为了估计这两种区域的更精确的传输，我们提出了一种基于自动图像消光的图像融合去雾的传输融合策略。我们首先提取每个像素周围局部补丁的均值和方差，然后提出一种具有每个补丁均值和方差的二进制分类方法，以将输入图像粗略地划分为正常区域和明亮区域的二进制图。然后，我们对二进制图进行平滑和量化，以自动生成三元值的三图。因此，我们可以避免手动标记trimap的困难。图像和Trimap都输入到贝叶斯抠图方法中，用于正常和明亮区域的软分割，以生成Alpha贴图。采用暗通道先验（DCP）提取正常区域的透射图，而提出了一种改进的大气面纱校正（AVC）方法来生成明亮区域的另一种透射图。最后，我们建议使用Alpha贴图模糊地融合两个透射图，以进行最终图像去雾。实验结果表明，我们的方法明显优于现有方法。