An Improved Dark Channel Prior Image Dehazing Algorithm Based on Fusion Luminance Model

An improved dark channel prior image dehazing algorithm based on the fusion luminance model was proposed to deal with the failure of a dark channel prior in the sky. First, sky and non-sky areas were segmented using a Canny operator. Next, luminance transmission was constructed by simulating the depth of the scene using the luminance, which combined with the transmission of the dark channel to form the transmission of the sky area. A fast-guided filter was used to optimize the transmission map. The value of atmospheric light was selected as the median of the top 0.1% of the pixels with strong anti-interference ability. Finally, the haze-free image was restored using the atmospheric scattering model. Experimental results show that the algorithm could effectively recover the details of the image and suppress the halo phenomenon for the haze image including the sky area with appropriate brightness and natural color.