基于天空检测和超像素分割的图像去雾方法
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TP391

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广东省重点领域研发计划(2020B0101130018);广东省水利科技创新项目(2022-02,2024-08);广东省科学院专项资金(2024GDASZH-2024010101)


Single image dehazing based on sky detection and superpixel segmentation
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    摘要:

    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.

    Abstract:

    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.

    参考文献
    [1] Thanh L T,Thanh D N H,Hue N M,et al.Single image dehazing based on adaptive histogram equalization and linearization of gamma correction[C]//2019 25th Asia-Pacific Conference on Communications (APCC).November 6-8,2019,Ho Chi Minh City,Vietnam.IEEE,2019:36-40
    [2] Xu Z Y,Liu X M,Chen X N.Fog removal from video sequences using contrast limited adaptive histogram equalization[C]//2009 International Conference on Computational Intelligence and Software Engineering.December 11-13,2009,Wuhan,China.IEEE,2009:1-4
    [3] Enesi I,Miho R.A fast algorithm for contrast restoration of weather degraded images[C]//2012 Sixth International Conference on Complex,Intelligent,and Software Intensive Systems.July 4-6,2012,Palermo,Italy.IEEE,2012:636-641
    [4] Zotin A.Fast algorithm of image enhancement based on multi-scale retinex[J].Procedia Computer Science,2018,131:6-14
    [5] Du Y,Guindon B,Cihlar J.Haze detection and removal in high resolution satellite image with wavelet analysis[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(1):210-217
    [6] 刘佳敏,何宁.基于改进同态滤波的低对比度图像增强[J].计算机应用与软件,2020,37(3):220-224 LIU Jiamin,HE Ning.Low contrast image enhancement based on improved homomorphic filtering[J].Computer Applications and Software,2020,37(3):220-224
    [7] He K M,Sun J,Tang X O.Single image haze removal using dark channel prior[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.June 20-25,2009,Miami,FL.IEEE,2009:1956-1963
    [8] Berman D,Treibitz T,Avidan S.Non-local image dehazing[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).June 27-30,2016,Las Vegas,NV,USA.IEEE,2016:1674-1682
    [9] Zhu Q S,Mai J M,Shao L.A fast single image haze removal algorithm using color attenuation prior[J].IEEE Transactions on Image Processing:a Publication of the IEEE Signal Processing Society,2015,24(11):3522-3533
    [10] He K M,Sun J,Tang X O.Guided image filtering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(6):1397-1409
    [11] 代书博,徐伟,朴永杰,等.基于暗原色先验的遥感图像去雾方法[J].光学学报,2017,37(3):0328002 DAI Shubo,XU Wei,PIAO Yongjie,et al.Remote sensing image defogging based on dark channel prior[J].Acta Optica Sinica,2017,37(3):0328002
    [12] 谭伟,曹世翔,齐文雯,等.一种高分辨率遥感图像去雾霾方法[J].光学学报,2019,39(3):48-58 TAN Wei,CAO Shixiang,QI Wenwen,et al.A haze removal method for high-resolution remote sensing images[J].Acta Optica Sinica,2019,39(3):48-58
    [13] 廖章回,姜闯.高分辨率遥感影像快速去雾[J].测绘学报,2022,51(3):446-456 LIAO Zhanghui,JIANG Chuang.Fast dehaze of high resolution remote sensing images[J].Acta Geodaetica et Cartographica Sinica,2022,51(3):446-456
    [14] Cai B L,Xu X M,Jia K,et al.DehazeNet:an end-to-end system for single image haze removal[J].IEEE Transactions on Image Processing:a Publication of the IEEE Signal Processing Society,2016,25(11):5187-5198
    [15] McCartney E J.Optics of the atmosphere:scattering by molecules and particles[M].New York:John Wiley and Sons,1976:23-32
    [16] 杨燕,张浩文,张金龙.结合天空分割和透射率映射的图像去雾[J].光学 精密工程,2021,29(2):400-410 YANG Yan,ZHANG Haowen,ZHANG Jinlong.Single image dehazing combining sky segmentation and transmission mapping[J].Optics and Precision Engineering,2021,29(2):400-410
    [17] 金天虎,陶砚蕴,李佐勇.基于超像素图像分割的暗通道先验去雾改进算法[J].电子学报,2023,51(1):146-159 JIN Tianhu,TAO Yanyun,LI Zuoyong.An improved dark channel prior dehazing algorithm based on superpixel image segmentation[J].Acta Electronica Sinica,2023,51(1):146-159
    [18] Geraets W G M,van Daatselaar A N,Verheij J G C.An efficient filling algorithm for counting regions[J].Computer Methods and Programs in Biomedicine,2004,76(1):1-11
    [19] Zhu Y Y,Tang G Y,Zhang X Y,et al.Haze removal method for natural restoration of images with sky[J].Neurocomputing,2018,275:499-510
    [20] Achanta R,Shaji A,Smith K,et al.SLIC superpixels compared to state-of-the-art superpixel methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282
    [21] 刘海波,杨杰,吴正平,等.基于区间估计的单幅图像快速去雾[J].电子与信息学报,2016,38(2):381-388 LIU Haibo,YANG Jie,WU Zhengping,et al.Fast single image dehazing based on interval estimation[J].Journal of Electronics & Information Technology,2016,38(2):381-388
    [22] Zhang Y F,Ding L,Sharma G.HazeRD:an outdoor scene dataset and benchmark for single image dehazing[C]//2017 IEEE International Conference on Image Processing (ICIP).September 17-20,2017,Beijing,China.IEEE,2017:3205-3209
    [23] Horé A,Ziou D.Image quality metrics:PSNR vs.SSIM[C]//2010 20th International Conference on Pattern Recognition.August 23-26,2010,Istanbul,Turkey.IEEE,2010:2366-2369
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引用本文

高仁强,陈亮雄,孙秀峰,王欢欢,高真.基于天空检测和超像素分割的图像去雾方法[J].南京信息工程大学学报(自然科学版),2024,16(5):630-642
GAO Renqiang, CHEN Liangxiong, SUN Xiufeng, WANG Huanhuan, GAO Zhen. Single image dehazing based on sky detection and superpixel segmentation[J]. Journal of Nanjing University of Information Science & Technology, 2024,16(5):630-642

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  • 收稿日期:2024-01-09
  • 在线发布日期: 2024-10-30
  • 出版日期: 2024-09-28

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