针对经典图像去雾算法在边缘区域易产生光晕效应、天空等明亮区域还原失真、色调偏移等问题,提出一种基于天空检测和超像素分割的改进暗通道图像去雾新方法(Dark Channel Prior based on Sky Detection and Super Pixel,SSPDCP).首先对雾图采用HSV变换提取亮度分量进行自适应阈值分割;然后应用图像连通分析技术识别天空域;接着利用天空域估计大气光值,针对天空和非天空区域分别建立各自的透射率计算模型,并基于构建的超像素级透射率融合模型获得融合透射率图,以促进边界区域的平滑过渡,采用多尺度引导滤波精化透射率图;最后应用大气散射模型完成图像复原并进行亮度增强处理,实现无雾图像的自然恢复.该方法识别的天空区域较为连续完整,以超像素代替方形窗口可以有效克服局部块效应的影响,大气光值和透射率图估计更为客观准确.从主观定性和客观定量评价方面来看,该方法复原的图像具有整体误差小、信噪比优良、结构相似度高等优势.本文所提出的图像去雾新方法能有效抑制边缘区域的光晕效应,且复原的天空区域明亮自然,图像去雾质量相比现有方法有进一步提升.
Single image dehazing based on sky detection and superpixel segmentation
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 ap-plies HSV color transformation to hazy images to extract the brightness component for adaptive-threshold segmenta-tion.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 fur-ther refined by multi-scale guided filtering.Finally,the dehazed image is naturally restored via the atmospheric scat-tering 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 ef-fectively mitigates halo effects in acquiring transmittance maps.The estimation of atmospheric light values and trans-mittance 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.
image dehazingsuperpixel segmentationdark channel priorsky recognitionatmospheric scattering model
万方数据
维普
