To solve the unnatural restoration of the sky area and imprecise estimation of haze density， a dehazing algorithm for sky segmentation and haze density estimation is proposed. First， to improve the precision of transmission estimation and the quality of image dehazing， the thresholds of gradient and brightness are used to segment the sky region. Next， an adaptive dark channel prior and quadratic tree subdivision method are utilized to estimate the atmospheric light. Finally， different transmission estimation methods are used for the sky and non-sky regions； a bright channel prior is used in the sky region， and a linear haze density estimation model is proposed in the non-sky region. The final transmission is obtained by combining the probability distribution of the pixel and edge refinement using guided filter， and the recovered image is attained using the atmospheric scattering model. Experimental results show that the dehazed images perform well in terms of subjective and objective quality evaluation. The proposed dehazing algorithm can restore a more natural sky and dehaze more thoroughly to improve the clarity of image details. The operating speed of the proposed algorithm is similar to that of the current algorithms. Furthermore， the proposed algorithm is more stable compared to traditional algorithms for different hazy scenes.