A novel contrast and saturation prior for image dehazing

Images captured in bad weather conditions such as fog, mist, haze, etc., are severely degraded due to the scattering of the particles in the atmosphere. These images are inappropriate for various applications of computer vision, e.g., transportation, remote sensing, video surveillance object recognition, etc. Image dehazing is the process of removing the haze effect from an image so that these applications can be benefited. The physical model of haze formation is used to restore a hazy image which requires two parameters to estimate: transmission and airlight. The accuracy of the dehazing depends on the estimation of the transmission. Dark channel prior (DCP) is an effective method to compute the transmission. However, a dark channel underestimates the transmission when an object in the scene has a similar color to the atmospheric light or sky region, as a result, the dehazed image looks dark. In this paper, we explore the DCP from a new perspective and reformulate it into contrast, saturation and brightness. We proposed a method to estimate the transmission without computing the dark channel. To overcome the problem of over-enhancement and remove the haze effect, a nonlinear model based on inverse strategy is introduced. It prevents the transmission from becoming over-estimated or under-estimated. The experimental result section demonstrates the efficacy of the proposed method over the natural and synthetic hazy images along with qualitative and quantitative analysis.