Leveraging ordinal depth for accurate transmission estimation in single image dehazing

Real-time images with haze do not meet the input quality standards for computer vision models. Existing dehazing algorithms have shown effective results at the nearby pixels. However, they fail to dehaze the pixels as the distance regions. Also, some of the models fail when the group of pixels contains white patches such as clouds. This paper proposed an approach that not only clears haze in nearby regions but also distant sky regions. The proposed model estimates image transmission based on the scene's depth information. The ordinal depth of the scene is first estimated and categorized as foreground, background, and sky. Using the transmission values of the foreground pixels, the transmission values for the other pixels are recalculated and applied to the Atmospheric Scattering model to produce the dehazed image. Extensive testing on datasets has shown that the proposed model delivers superior results compared to existing state-of-the-art image dehazing networks.