Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we propose a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.

Bad weather (eg., fog and haze) significantly degrades the quality of outdoor images taken by camera, leading to the fact that most automatic systems, which strongly depends on the definition of the input images, fail to work normally. Thus, the improvement of the dehazing technology is highly desired. To overcome the disadvantages of traditional dark channel priorbased algorithm, we propose a more efficient dehazing algorithm combining dark channel prior and mean shift segmentation. Firstly, we take the operation of white balance on the input haze image to reduce the negative influence of color cast. Secondly, we use the mean shift segmentation algorithm to separate the sky regions from the foreground in the transmission map, which is obtained with the dark channel prior-based approach. Thirdly, we enhance the brightness of the sky regions in the transmission map independently and use guided image filtering to smooth the map. Finally, we restore the image with the re-refined transmission map. The experimental results demonstrate that the proposed approach is better able to handle the sky regions and solve the problem of color cast compared with the typical dehazing algorithms.