Single Image Dehazing Algorithm Based on Improved Dark Channel Prior and Guided Filter
-
摘要: 针对单幅雾霾图像中包含的大面积天空或白色物体等区域暗通道先验失效和导向滤波去雾方法去雾不彻底的问题, 提出了一种基于改进暗通道和导向滤波的单幅图像去雾算法.首先基于暗通道引入了混合暗通道, 然后对混合暗通道进行映射处理, 从而得到大气耗散函数粗估计值; 利用导向滤波方法优化大气耗散函数粗估计值, 进而求解环境光值和初始传输图; 利用全变差正则化方法对初始传输图进行优化, 以解决其平滑性较差的问题.实验结果表明, 本文算法得到的去雾图像具有较高的清晰度, 对于大面积天空或白色物体区域也能实现良好的去雾效果.Abstract: To tackle the problem that dark channel prior is invalid for large sky or bright objects regions of single hazy image and the problem that some hazes cannot be removed using guided filter, we propose a single image dehazing algorithm based on improved dark channel prior and guided filter. Firstly, we introduce a mixed dark channel based on dark channel. The coarser estimate of atmosphere veil is obtained after a map processing on the mixed dark channel. Then guided filter is utilized to optimize the coarser atmosphere veil. An initial transmission map can be obtained after getting more accurate atmospheric light with the optimized atmosphere veil. Finally, the total variation regularization method is utilized to address the problem of poor smoothness of the initial transmission map. Experimental results show that the recovered haze-free image with the proposed method has better sharpness, even for large sky or bright objects regions.
-
Key words:
- Image dehazing /
- dark channel prior /
- guided filter /
- total variation
-
图 2 混合暗通道改进前后去雾效果对比结果
Fig. 2 The comparative results of improved algorithm and unimproved mixed dark channel
图 4 全变差优化前后去雾效果对比结果
Fig. 4 The comparative results of improved algorithm and unimproved total variation filter
图 7 包含大面积天空雾霾图像去雾效果对比
Fig. 7 Comparison with others0 work in hazy images with large sky regions
-
[1] Nayar S K, Narasimhan S G. Vision in bad weather. In:Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece:IEEE, 1999. 820-827 [2] Narasimhan S G, Nayar S K. Chromatic framework for vision in bad weather. In:Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, SC, USA:IEEE, 2000. 598-605 [3] Narasimhan S G, Nayar S K. Contrast restoration of weather degraded images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(6):713-724 doi: 10.1109/TPAMI.2003.1201821 [4] Schechner Y Y, Narasimhan S G, Nayar S K. Polarization-based vision through haze. Applied Optics, 2003, 42(3):511-525 doi: 10.1364/AO.42.000511 [5] Namer E, Schechner Y Y. Advanced visibility improvement based on polarization filtered images. In:Proceedings of the 2005 SPIE 5888, Polarization Science and Remote Sensing II. San Diego, USA:SPIE, 2005. 36-45 [6] Tan R T. Visibility in bad weather from a single image. In:Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA:IEEE, 2008. 1-8 http://www.oalib.com/references/16892104 [7] Fattal R. Single image dehazing. ACM Transactions on Graphics, 2008, 27(3):Article No. 72 http://www.oalib.com/references/15079874 [8] He K M, Sun J, Tang X O. Single image haze removal using dark channel prior. In:Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA:IEEE, 2009. 1956-1963 [9] 吴迪, 朱青松.图像去雾的最新研究进展.自动化学报, 2015, 41(2):221-239 http://www.aas.net.cn/CN/abstract/abstract18603.shtmlWu Di, Zhu Qing-Song. The latest research progress of image dehazing. Acta Automatica Sinica, 2015, 41(2):221-239 http://www.aas.net.cn/CN/abstract/abstract18603.shtml [10] He K M, Sun J, Tang X O. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6):1397-1409 doi: 10.1109/TPAMI.2012.213 [11] Pang J H, Au O C, Guo Z. Improved single image dehazing using guided filter. In:Proceedings of the 2011 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. Xi'an, China:Asia-Pacific Signal and Information Processing Association, Hong Kong, 2011. 522-525 [12] Shi Z W, Long J, Tang W, Zhang C S. Single image dehazing in inhomogeneous atmosphere. Optik-International Journal for Light and Electron Optics, 2014, 125(15):3868-3875 doi: 10.1016/j.ijleo.2014.01.170 [13] Tarel J P, Hautiere N. Fast visibility restoration from a single color or gray level image. In:Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan:IEEE, 2009. 2201-2208 http://www.oalib.com/references/16889213 [14] 禹晶, 李大鹏, 廖庆敏.基于物理模型的快速单幅图像去雾方法.自动化学报, 2011, 37(2):143-149 doi: 10.3724/SP.J.1004.2011.00143Yu Jing, Li Da-Peng, Liao Qing-Min. Physics-based fast single image fog removal. Acta Automatica Sinica, 2011, 37(2):143-149 doi: 10.3724/SP.J.1004.2011.00143 [15] Liu X, Zeng F X, Huang Z T, Ji Y F. Single color image dehazing based on digital total variation filter with color transfer. In:Proceedings of the 20th IEEE International Conference on Image Processing. Melbourne, Australia:IEEE, 2013. 909-913 [16] 张小刚, 唐美玲, 陈华, 汤红忠.一种结合双区域滤波和图像融合的单幅图像去雾算法.自动化学报, 2014, 40(8):1733-1739 http://www.aas.net.cn/CN/abstract/abstract18440.shtmlZhang Xiao-Gang, Tang Mei-Ling, Chen Hua, Tang Hong-Zhong. A dehazing method in single image based on double-area filter and image fusion. Acta Automatica Sinica, 2014, 40(8):1733-1739 http://www.aas.net.cn/CN/abstract/abstract18440.shtml [17] 刘海波, 杨杰, 吴正平, 张庆年, 邓勇.基于暗通道先验和Retinex理论的快速单幅图像去雾方法.自动化学报, 2015, 41(7):1264-1273 http://www.aas.net.cn/CN/abstract/abstract18700.shtmlLiu Hai-Bo, Yang Jie, Wu Zheng-Ping, Zhang Qing-Nian, Deng Yong. A fast single image dehazing method based on dark channel prior and Retinex theory. Acta Automatica Sinica, 2015, 41(7):1264-1273 http://www.aas.net.cn/CN/abstract/abstract18700.shtml [18] Tang K T, Yang J C, Wang J. Investigating haze-relevant features in a learning framework for image dehazing. In:Proceedings of the 2014 IEEE International Conference on Computer Vision and Pattern Recognition. Columbus, Ohio, USA:IEEE, 2014. 2995-3002 [19] Wang Y K, Fan C T. Single image defogging by multiscale depth fusion. IEEE Transactions on Image Processing, 2014, 23(11):4826-4837 doi: 10.1109/TIP.2014.2358076 [20] 褚宏莉, 李元祥, 周则明, 沈霁.基于黑色通道的图像快速去雾优化算法.电子学报, 2013, 41(4):791-797 http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201304027.htmChu Hong-Li, Li Yuan-Xiang, Zhou Ze-Ming, Shen Ji. Optimized fast dehazing method based on dark channel prior. Acta Electronica Sinica, 2013, 41(4):791-797 http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201304027.htm [21] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D:Nonlinear Phenomena, 1992, 60(1-4):259-268 doi: 10.1016/0167-2789(92)90242-F [22] Boyd S, Parikh N, Chu E, Peleato B, Eckstein J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 2010, 3(1):1-122 doi: 10.1561/2200000016 [23] Goldstein T, Osher S. The split Bregman method for L1-regularized problems. SIAM Journal on Imaging Sciences, 2009, 2(2):323-343 doi: 10.1137/080725891 -
下载:
点击查看大图
图(10) / 表(1)
计量
- 文章访问数: 3028
- HTML全文浏览量: 408
- PDF下载量: 956
- 被引次数: 0
