Outdoor images captured in bad weather are prone to yield poor visibility, which is a fatal problem for most computer vision\napplications. The majority of existing dehazing methods rely on an atmospheric scattering model and therefore share a common\nlimitation; that is, themodel is only validwhen the atmosphere is homogeneous. In this paper, we propose an improved atmospheric\nscattering model to overcome this inherent limitation. By adopting the proposed model, a corresponding dehazing method is also\npresented. In this method, we first create a haze density distribution map of a hazy image, which enables us to segment the hazy\nimage into scenes according to the haze density similarity.Then, in order to improve the atmospheric light estimation accuracy, we\ndefine an effective weight assignment function to locate a candidate scene based on the scene segmentation results and therefore\navoid most potential errors. Next, we propose a simple but powerful prior named the average saturation prior (ASP), which is a\nstatistic of extensive high-definition outdoor images. Using this prior combined with the improved atmospheric scattering model,\nwe can directly estimate the scene atmospheric scattering coefficient and restore the scene albedo.The experimental results verify\nthat our model is physically valid, and the proposed method outperforms several state-of-the-art single image dehazing methods\nin terms of both robustness and effectiveness.
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