An Efficient Image Haze Removal Algorithm Using Color Attenuation Prior

We present a method of recovering high dynamic range radiance maps from photographs taken with conventional imaging equipment. In our method, multiple photographs of the scene are taken with different amounts of exposure. Here proposed scheme states that a simple but powerful color attenuation prior for haze removal from a single input hazy image. Here we create a linear model for modeling the scene depth of the hazy image under this novel prior and learning a parameters of the model with a supervised learning process, the depth information can be well recovered. With a depth map of hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove a 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. We discuss how this work is applicable in many areas such as computer graphics we involve digitized photographs, including image-based modeling, image compositing, and image processing. Finally we demonstrate a few applications which is having high dynamic range radiance maps, such as synthesizing realistic motion blur and simulating the response of the human visual system.