Abstract: Defogging algorithms based on the dark channel prior(DCP) may cause color distortion and darker brightness when they are applied on images with large sky region. To improve the defogging efficiency of DCP based algorithms, a new fog removal method based on sky segmentation and tone mapping is proposed in this paper. This method first designs a sky recognition algorithm in the HSI color space using image modal and image unicom area to separate the sky region from the non-sky region, next calculates the transmission map of the two regions respectively according to DCP and estimates the atmospheric light value using the sky area, then recovers the image in the RGB color space using the atmospheric scattering model. The defogged image is finally produced by further utilizing an improved adaptive tone mapping. The experimental results with synthetic fog map, real fog map and fog map from network show that the images resulted from our method have better objective indicator and fog removal effect compared with some state-of-the-art methods including three deep learning based ones.