A haze removal method for unmanned aerial vehicle images based on robust estimation of atmospheric light
-
摘要:
针对无人机（UAV）获取的图像易受雾、霾等天气影响导致图像质量降低的问题，本文提出一种基于大气光鲁棒估计的无人机图像去雾方法。首先，选取具有不同表面反照率的像素块，得到各个图像块的像素直线，利用各条像素直线与大气光共面的性质，估计得到大气光的方向；然后，利用无人机对地成像时图像各像素点的景深相似的特点，定义了图像的全局透射率，通过全局透射率和各像素直线在大气光方向上的投影计算得到大气光幅度；最后，通过对雾天图像模型进行变换得到无雾图像。为使本文方法适用于不同类型的图像，采用了自动调整图像块尺寸和条件阈值等措施来提高方法的鲁棒性。通过真实无人机图像的去雾实验证明，相比现有的图像去雾方法，本文方法在去雾的视觉效果和客观评价指标上都有较大的提升。
Abstract:Aimed at the problem that the quality of the images acquired by unmanned aerial vehicle (UAV) is easily reduced due to the fog or haze weather, a haze removal algorithm for UAV images based on robust estimation of atmospheric light was proposed. The proposed algorithm selects image patches with different surface reflectance rate to obtain the pixel line of each patch. Using the properties that all the pixel lines are coplanar with the atmospheric light, the orientation of the atmospheric light vector was calculated. Based on the fact that scene depths of each pixel in the image are similar, the global transmittance is defined. The amplitude of the atmospheric light and the dehazed image are obtained using the global transmittance and projection of the pixel lines on the direction of the atmospheric light. In order to apply this method to different types of images, the measures of automatic adjustment of image block size and condition threshold were adopted to improve the robustness of the algorithm. The experimental results with the real UAV images show that the proposed algorithm has a great improvement in the visual effect and objective evaluation index compared with the existing methods.
-
-
[1] 刘海波, 杨杰, 吴正平, 等.基于暗通道先验和Retinex理论的快速单幅图像去雾方法[J].自动化学报, 2015, 41(7):1264-1273. http://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201507005.htmLIU H B, YANG J, WU Z P, et al.A fast single image dehazing method based on dark channel prior and Retinex theory[J].Acta Automatica Sinica, 2015, 41(7):1264-1273(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201507005.htm [2] 张登银, 鞠铭烨, 王雪梅.一种基于暗通道先验的快速图像去雾算法[J].电子学报, 2015, 43(7):1437-1443. http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201507029.htmZHANG D Y, JU M Y, WANG X M.A fast image haze removal algorithm using dark channel prior[J].Acta Electronica Sinica, 2015, 43(7):1437-1443(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201507029.htm [3] NARASIMHAN S G, NAYAR S K.Vision and the atmosphere[J].International Journal of Computer Vision, 2002, 48(3):233-254. doi: 10.1023/A:1016328200723 [4] 周雨薇, 陈强, 孙权森, 等.结合暗通道原理和双边滤波的遥感图像增强[J].中国图象图形学报, 2014, 19(2):313-321. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201402018.htmZHOU Y W, CHEN Q, SUN Q S, et al.Remote sensing image enhancement based on dark channel prior and bilateral filtering[J].Journal of Image and Graphics, 2014, 19(2):313-321(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201402018.htm [5] LAI Y H, CHEN Y L, CHIOU C J, et al.Single-image dehazing via optimal transmission map under scene priors[J].IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(1):1-14. doi: 10.1109/TCSVT.2014.2329381 [6] 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. doi: 10.1109/TPAMI.2003.1201821 [7] FATTAL R.Single image dehazing[J].ACM Transactions on Graphics, 2008, 27(3):1-9. http://dl.acm.org/citation.cfm?id=1360612.1360671 [8] SULAMI M, GLATZER I, FATTAL R, et al.Automatic recovery of the atmospheric light in hazy images[C]//2014 IEEE International Conference on Computational Photography (ICCP).Piscataway, NJ:IEEE Press, 2014:1-11. [9] TAN R T. Visibility in bad weather from a single image[C]//IEEE Conference Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2008:1-8. [10] 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. doi: 10.1109/TPAMI.2010.168 [11] TAREL J P, HAUTIERE N.Fast visibility restoration from a single color or gray level image[C]//IEEE 12th International Conference on Computer Vision.Piscataway, NJ:IEEE Press, 2009:2201-2208. [12] 姚鹏, 王宏伦, 刘畅.基于动态流体扰动原理的三维滚动航路规划[J].北京航空航天大学学报, 2015, 41(12):2280-2287. http://bhxb.buaa.edu.cn/CN/abstract/abstract13641.shtmlYAO P, WANG H L, LIU C.Three-dimensional rolling path planning via dynamic fluid disturbance[J].Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(12):2280-2287(in Chinese). http://bhxb.buaa.edu.cn/CN/abstract/abstract13641.shtml [13] LIU S, LI H, YUAN Y, et al.A method for uav real-time image simulation based on hierarchical degradation model[J].Advances in Intelligent Systems and Computing, 2014, 277:221-232. doi: 10.1007/978-3-642-54924-3 [14] MATEOS G, GIANNAKIS G B.Robust PCA as bilinear decomposition with outlier-sparsity regularization[J].IEEE Transactions on Signal Processing, 2012, 60(10):5176-5190. doi: 10.1109/TSP.2012.2204986 [15] MCCARTNEY E J.Optics of the atmosphere:scattering by molecules and particles[M].New York:Wiley, 1976. [16] HAUTIERE N, TAREL J P, AUBERT D, et al.Blind contrast enhancement assessment by gradient ratioing at visible edges[J].Image Analysis and Stereology, 2008, 27(2):87-95. http://www.oalib.com/paper/2351811 [17] JOBSON D J, RAHMAN Z, WOODELL G A.Statistics of visual representation[C]//Proceedings of the 2002 Visual Information Processing XI.Bellingham:SPIE, 2002:25-35. [18] HUANG Y, DING W, LI H.Haze removal for UAV reconnaissance images using layered scattering model[J].Chinese Journal of Aeronautics, 2016, 29(2):502-511. doi: 10.1016/j.cja.2016.01.012 -
下载:
点击查看大图
计量
- 文章访问数: 1004
- HTML全文浏览量: 246
- PDF下载量: 392
- 被引次数: 0
