单幅航片的薄云去除方法
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摘要
与匀质的雾霾不同,航摄像片中的絮状薄云形态多变、厚薄不均,航片的薄云去除比图像去雾更具挑战性.针对单幅航片中的薄云,提出一种基于分治思想的云去方法.首先设计一种基于颜色衰减先验、表征薄云覆盖程度的遮罩指数,并依据该指数将含云航片分割为遮罩层次不同的区块,使区块内的薄云近乎均匀、满足传输率光滑的假设;然后以改进的颜色线方法分别计算各区块内的大气光值,构成整幅图像的大气光图;再应用基于边界约束的传输率估算法计算粗略传输图;最后以加权L1范数作规则化项对粗略传输图进行优化,获得精细传输图,结合含云图像、大气光图与精细传输图反求理想条件下的航摄图像.在单机环境下进行单幅航片的薄云去除的实验结果表明,该方法能够大幅消减甚至完全去除絮状薄云对航片的遮罩影响,有效地增强图像可见性.
Due to non-uniformity, cloud in an aerial photo is even harder to remove than haze. This paper proposes a divide-and-conquer based method of cloud removal using a single aerial photo. Based on color attenuation prior, we design a veiling index that indicates the local concentration of cloud on the aerial photo. By this index, an image veiled by cloud was segmented into various regions of various extents of veiling. Each region was veiled by nearly uniform cloud, and thus subjected to commonly used assumption such as boundary constraint on transmission in image patches. Atmospheric light vectors in each region were estimated by modified color-lines model and then composed into a spatially varying airlight map for the whole image. Initial estimated scene transmission was further optimized by a weighted L1-norm based contextual regularization, which was used to obtain scene reflection image thereafter. We demonstrate the effectiveness of our cloud removal method on a number of example aerial photos containing cloud and compare our results with that of existing relevant methods.
Due to non-uniformity, cloud in an aerial photo is even harder to remove than haze. This paper proposes a divide-and-conquer based method of cloud removal using a single aerial photo. Based on color attenuation prior, we design a veiling index that indicates the local concentration of cloud on the aerial photo. By this index, an image veiled by cloud was segmented into various regions of various extents of veiling. Each region was veiled by nearly uniform cloud, and thus subjected to commonly used assumption such as boundary constraint on transmission in image patches. Atmospheric light vectors in each region were estimated by modified color-lines model and then composed into a spatially varying airlight map for the whole image. Initial estimated scene transmission was further optimized by a weighted L1-norm based contextual regularization, which was used to obtain scene reflection image thereafter. We demonstrate the effectiveness of our cloud removal method on a number of example aerial photos containing cloud and compare our results with that of existing relevant methods.
引文
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[2]Xu Zhiyuan,Liu Xiaoming.Enhancement algorithm of fog-degraded images based on bilinear interpolation dynamic histogram equalization[J].Journal of Dalian Maritime University,2010,36(3):64-68(in Chinese)(许志远,柳晓鸣.基于双线性插值动态直方图均衡化的雾天图像增强算法[J].大连海事大学学报,2010,36(3):64-68)
[3]Parthasarathy S,Sankaran P.A retinex based haze removal method[C]//Proceedings of the 7th International Conference on Industrial and Information Systems.Los Alamitos:IEEEComputer Society Press,2012:1-6
[4]Kimmel R,Elad M,Shaked D,et al.A variational framework for retinex[J].International Journal of Computer Vision,2003,52(1):7-23
[5]Tan R T.Visibility in bad weather from a single image[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2008:1-8
[6]Kopf J,Neubert B,Chen B,et al.Deep photo:model-based photograph enhancement and viewing[J].ACM Transactions on Graphics,2008,27(5):Article No.116
[7]Fattal R.Single image dehazing[J].ACM Transactions on Graphics,2008,27(3):Article No.72
[8]Sulami M,Geltzer I,Fattal R,et al.Automatic recovery of the atmospheric light in hazy images[C]//Proceedings of the IEEEInternational Conference on Computational Photography.Los Alamitos:IEEE Computer Society Press,2014:1-11
[9]Fattal R.Dehazing using color-lines[J].ACM Transactions on Graphics,2014,34(1):Article No.13
[10]He K M,Sun J,Tang X O.Single image haze removal using dark channel prior[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(12):2341-2353
[11]He K M,Sun J,Tang X O.Guided image filtering[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2013,35(6):1397-1409
[12]Meng G.F,Wang Y,Duan J Y,et al.Efficient image dehazing with boundary constraint and contextual regularization[C]//Proceedings of the IEEE International Conference on Computer Vision.Los Alamitos:IEEE Computer Society Press,2013:617-624
[13]Narasimhan S G,Nayar S K.Contrast restoration of weather degraded images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(6):713-724
[14]Zhu Q S,Mai J M,Shao L.A fast single image haze removal algorithm using color attenuation prior[J].IEEE Transactions on Image Process,2015,24(11):3522-3533
[15]Zhang Q,Xiao C X.Cloud detection of RGB color aerial photographs by progressive refinement scheme[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(11):7264-7275
[16]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
[17]Omer I,Werman M.Color lines:image specific color representation[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2004,2:946-953