To solve the halo effect in edge area and color distortion problems in sky area of the classic image dehazing algorithm, a new improved dark channel prior dehazing method based on sky detection and super-pixel segmentation is proposed. The proposed method first applied the HSV color transformation to extract the light component of haze image with an adaptive-threshold segmentation technology. Then sky and non-sky areas were extracted with image connectivity analysis technology. After that, the atmospheric light value was estimated from sky areas, and transmission map of sky and non-sky areas were estimated with luminance model and super-pixels dark channel prior model separately, and a pixel-based fusion model was proposed to obtain a comprehensive transmission map to promote the smooth transition in boundary area. Next, the transmission map was refined by a multi-scale guided filter algorithms. Finally, the dehazed image was restored naturally with atmospheric scattering model and brightness enhancement processing. Experimental results show that the sky area identified from this method is more continuously and completely, and it is effectively to overcome the influence of halo effects when use superpixels instead of square windows to acquire transmission maps. The estimation of atmospheric light values and transmittance maps are more objectively and accurately. From the perspectives of subjective qualitative and objective quantitative evaluation, the proposed method shows more advantage over small overall error, excellent signal-to-noise ratio, and high structural similarity in dehazed images. The proposed method can restore a more natural sky and weaken the halo effect in edge area, the quality of the dehazed image is further improved compared to the state of the art on both qualitative and quantitative analysis.