透射率和大气光自适应估计的暗通道去雾
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摘要
针对暗通道先验理论去雾算法景深突变区域容易出现光晕、不满足暗通道先验理论区域出现颜色失真、大气光估计不准确容易导致偏色现象,提出了自适应透射率和大气光估计的去雾算法。通过Canny算子结合形态学膨胀运算确定易出现光晕效应的区域,在该区域上采用像素等级差诱导高斯核函数得到的自适应高斯滤波器来消除光晕效应。其次,提出一种天空区域透射率估计方法处理因不满足暗原色原理出现的天空区域颜色失真问题。最后,提出先估计大气光方向,再最大化大气光方向与前1%最亮像素点的相似度的大气光估计方法。实验表明,该算法获得的透射率图精细,复原图像自然,能够有效去除光晕效应,修正天空区域的颜色失真。
In haze removal based on dark channel prior theory, the main failure includes: 1) halo effect in the area with depth mutation; 2) color distortion in the field where the dark channel prior theory is not satisfied; 3) color cast due to inaccurate estimation of airlight. In this paper, an adaptive transmission and airlight estimating method was proposed. Canny operator and dilation operation were applied in hallo-effect area detection; on the other hand, an adaptive Gaussian filter based on pixel-level Gaussian kernel function was used to eliminate the halo effect. A method of estimating sky transmission was applied to deal with the color distortion because of dissatisfaction of dark channel prior. Finally, a new airlight estimating method was proposed: first, the direction of airlight was estimated; second, the top 1% brightest pixels were picked; third,the pixel with the highest similarity to the estimated direction of airlight was regarded as airlight. The experiment results show that the proposed approach can achieve refined transmission image, unaffected recovery pictures. Besides, the proposed method can effectively eliminate the halo effect, revise the color cast of sky region.
In haze removal based on dark channel prior theory, the main failure includes: 1) halo effect in the area with depth mutation; 2) color distortion in the field where the dark channel prior theory is not satisfied; 3) color cast due to inaccurate estimation of airlight. In this paper, an adaptive transmission and airlight estimating method was proposed. Canny operator and dilation operation were applied in hallo-effect area detection; on the other hand, an adaptive Gaussian filter based on pixel-level Gaussian kernel function was used to eliminate the halo effect. A method of estimating sky transmission was applied to deal with the color distortion because of dissatisfaction of dark channel prior. Finally, a new airlight estimating method was proposed: first, the direction of airlight was estimated; second, the top 1% brightest pixels were picked; third,the pixel with the highest similarity to the estimated direction of airlight was regarded as airlight. The experiment results show that the proposed approach can achieve refined transmission image, unaffected recovery pictures. Besides, the proposed method can effectively eliminate the halo effect, revise the color cast of sky region.
引文
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[25]王欣欣,何明一,和人杰,等.基于半反图像的透射率优化降雾算法[J].电子设计工程,2014,22(22):181-184.
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[27]黄明晶,刘清,熊燕帆,等.面向内河雾天图像的大气光亮度估算方法研究[J].交通信息,2013,31(3):33-38.
[28]贾冬雪.图像与去雾算法的研究与应用[D].沈阳:沈阳工业大学,2015.
[29]南栋,毕笃彦,马附平,等.基于分类学习的去雾后图像质量评价算法[J].自动化学报,2016,42(2):270-278.
[2]李菊霞,余雪丽.雾天条件下的多尺度Retinex图像增强算法[J].计算机科学,2013,40(3):299-301.
[3]张鑫,王卫星,张元方,等.基于分数阶微分及改进Rentinex的模糊航空图像的增强[J].计算机应用研究,2015,32(9):2844-2848.
[4]林英.基于小波变换的同态滤波法去雾图像处理[J].龙岩学院学报,2008,26(6):32-36.
[5]ZHANG H,LIU X,HUANG Z,et al.Single image dehazing based on fast wavelete transform with weighted image fusion[C]//Proceedings of the 2014 IEEE International Conference on Image Processing.Piscataway:IEEE,2014:4542-4546.
[6]聂宁,吴四九,程卫东.基于小波变换的图像去雾研究[C]//Proceedings of the 2014 International Conference on Computer,Comunications and Information.Beijing:Atlantis Press,2014:399-403.
[7]TAN R T.Visibility in bad weather from a single image[C]//Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,2008:1-8.
[8]FATTAL R.Single image dehazing[J].ACM Transactions on Graphics,2008,27(3):Article No.72.
[9]HE K,SUN J,TANG X.Single image haze removal using dark channel prior[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(12):2341-2353.
[10]甘佳佳,肖春霞.结合精确大气散射图计算的图像快速去雾[J].中国图象图形学报,2013,18(5):583-590.
[11]TRIPATHI A K,MUKHOPADHYAY S.Single image fog removal using bilateral filter[C]//Proceedings of the 2012 IEEE International Conference on Signal Processing,Computing and Control.Piscataway:IEEE,2012:1-6.
[12]KIM S,PARK S,CHOI K.A system architecure for real time traffic monitor ing in foggy video[C]//Proceedings of the 201521st Korea-Japan Joint Workshop on Frontiers of Computer Vision.Piscataway:IEEE,2015:1-4.
[13]GIBSON K B,VD,NGUYEN T Q.An investigation of dehazing effects on image and video coding[C]//IEEE Transactions on Image Processing,2012,21(2):662-673.
[14]KUMARI A,THOMAS P T,SAHOO S K.Single image fog removal using gamma transformation and media filtering[C]//Proceedings of the 2014 Annual IEEE India Conference.Piscataway:IEEE,2014:1-5.
[15]刘红平,陈明义.基于二次滤波的单幅图像快速去雾算法研究[J].计算机工程与应用,2015,51(8):156-159.
[16]HE K,SUN J,TANG X.Guided image filtering[C//Proceedings of the 2010 European Conference on Computer Vision.Berlin:Springer,2010:1-14.
[17]石文轩,詹诗萦,李婕.一种边缘化的暗通道去雾算法[J].计算机应用研究,2013,30(12):3854-3856.
[18]钱晓燕.单一图像多滤波联合快速去雾算法[J].科学技术与工程,2015,15(6):236-240.
[19]陈露,和红杰,陈帆.基于边界邻域最大值滤波的快速图像去雾算法[J].光子学报,2014,43(11):11003-1-111003-6.
[20]张小刚,唐美玲,陈华,等.一种结合双区域滤波和图像融合的单幅图像去雾算法[J].自动化学报,2014,40(8):1733-1739.
[21]白海平,杨燕.基于暗通道先验的比值重估透射率去雾算法[J].计算机工程与应用,2016,52(13):212-217.
[22]催宝侠,贾冬雪,段勇.明亮区域的暗原色先验算法[J].沈阳工业大学学报,2015,37(1):75-79.
[23]SULAMI M,GLATZER I,FATTAL R,et al.Automatic recovery of the atmospheric light in hazy images[C]//Proceedings of the2014 International Conference on Computational Photography.Piscataway:IEEE,2014:1-11.
[24]刘言,张红英,吴亚东,等.基于半逆法的一种快速单幅图像去雾算法[J].图学学报,2015,36(1):68-76.
[25]王欣欣,何明一,和人杰,等.基于半反图像的透射率优化降雾算法[J].电子设计工程,2014,22(22):181-184.
[26]李加元,胡庆武,艾明耀,等.结合天空识别和暗通道原理的图像去雾[J].中国图象图形学报,2015,20(4):514-519.
[27]黄明晶,刘清,熊燕帆,等.面向内河雾天图像的大气光亮度估算方法研究[J].交通信息,2013,31(3):33-38.
[28]贾冬雪.图像与去雾算法的研究与应用[D].沈阳:沈阳工业大学,2015.
[29]南栋,毕笃彦,马附平,等.基于分类学习的去雾后图像质量评价算法[J].自动化学报,2016,42(2):270-278.