This paper proposes an optimized image dehazing algorithm that integrates sky region segmentation,dark channel prior (DCP),and bright channel prior (BCP) to address the limitations of traditional methods.such as image distortion,low contrast,over-saturation and poor sky-region handling. Firstly,the Shannon entropy of the gradient map is calculated from the hazy image to obtain the texture map of the foggy image,and the texture map can roughly determine the location of the sky region.Then,the sky region is precisely segmented by combining the region growing algorithm and the Canny operator,and the atmospheric light value is obtained in the final obtained sky region.In addition,this paper introduces the BCP theory and estimates the transmission rate by using the dark-bright channel priors.Finally,a clear haze-free image is obtained according to the atmospheric scattering model.Experimental results show that the proposed dehazed algorithm can effectively optimize the problem that the traditional dehazed algorithm fails in the sky region,and the obtained dehazing image is more satisfied with human visual experience.Furthermore,the algorithm achieves superior objective evaluation metrics,verifying its feasibility, effectiveness and superiority.