Image dehazing based on a transmission fusion strategy by automatic image matting

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.