To address issues perplexing classic image dehazing methods,including halo effect in edge regions,color distortion in bright areas like sky,and hue shifts,we propose a novel image dehazing approach based on improved dark channel prior (SSPDCP:Dark Channel Prior based on Sky Detection and Super Pixel).This approach first applies HSV color transformation to hazy images to extract the brightness component for adaptive-threshold segmentation.Then it utilizes image connectivity analysis to identify the sky regions,from which the atmospheric light value is estimated,and separate transmittance maps of sky and non-sky areas are computed with a luminance model and a superpixel segmentation-based dark channel prior model,respectively.Subsequently,a superpixel-based fusion model is proposed to obtain a comprehensive transmittance map,ensuring smooth transition in boundary areas,which is further refined by multi-scale guided filtering.Finally,the dehazed image is naturally restored via the atmospheric scattering model and brightness enhancement processing.Experimental results show that the proposed approach identifies sky regions more continuously and completely,moreover,by employing superpixels instead of square windows,it effectively mitigates halo effects in acquiring transmittance maps.The estimation of atmospheric light values and transmittance maps is more objective and accurate.Both subjective qualitative and objective quantitative evaluations reveal advantages such as low overall error,excellent signal-to-noise ratio,and high structural similarity in dehazed images.Compared to the state-of-the-art methods,the proposed approach restores skies more naturally,weakens halo effect in edge regions,and achieves qualitative and quantitative improvements in dehazing performance.