基于Color Lines先验的高阶马尔科夫随机场去雾

毕笃彦; 眭萍; 何林远; 马时平

doi:10.11999/JEIT151308

基于Color Lines先验的高阶马尔科夫随机场去雾

doi: 10.11999/JEIT151308

基金项目:

国家自然科学基金(61372167, 61379140)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-11-23
- 修回日期: 2016-04-15
- 刊出日期: 2016-09-19

Higher-order Markov Random Fields Defogging Based on Color Lines

Funds:

The National Natural Science Foundation of China (61372167, 61379140)

摘要

摘要: 传统的一阶马尔科夫随机场在图像先验信息表达和对图像整体的约束上能力有限，同时基于暗通道的去雾算法在天空等大片白色区域处理效果存在偏差。针对以上问题，该文提出一种基于Color Lines 的高阶马尔科夫随机场去雾算法。该算法通过引入对颜色失真具有很好鲁棒性的Color Lines 先验条件，初步校正经暗通道获取的传输图，然后利用高阶马尔科夫随机场优化传输图，获取最终精确的去雾图像。实验结果表明，与已有算法相比，该文算法具有更强的普适性，可提高雾天图像的清晰度，同时恢复更多的图像细节等信息。
- 图像去雾 /
- 暗通道先验 /
- 高阶马尔科夫随机场 /
- Color Lines
Abstract: Compared with the first-order Markov random fields, higher-order Markov random fields could incorporate more statistical priors, thus have much expressive power of modeling. And the defogged images which based on dark channel prior have much error in sky regions and big white blocks. To solve those problems, this paper proposes a Markov random fields defogging method based on Color Lines. This method corrects the dark channel prior, according to the color lines which has a good robustness to color distortion, then uses the higher-order Markov random fields to optimize the transmission image to obtain final defogged image. The experimental results show that this method could improve the image resolution, while maintaining more image details.
- Image defogging /
- Dark channel prior /
- Higher-order Markov random fields /
- Color Lines

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参考文献(15)

周妍, 李庆武, 霍冠英. 基于非下采样Contourlet变换系数直方图匹配的自适应图像增强[J]. 光学精密工程, 2014, 22(8): 2214-2222. doi: 10.3788/OPE.20142208.2214.

ZHOU Yan, LI Qingwu, and HUO Guanying. Adaptive image enhancement based on NSCT coefficient histogram matching [J]. Optics and Precision Engineering, 2014, 22(8): 2214-2222. doi: 10.3788/OPE.20142208.2214.

SEOW M J and ASARI V K. Ratio rule and homomorphic filter for enhancement of digital color image[J]. Neurocomputing, 2006, 69(7/9): 954-958. doi: 10.1016 /j.neucom.2005.07.003.

赵宏宇, 萧创柏, 禹晶. 马尔科夫随机场模型下的Retinex夜间彩色图像增强[J]. 光学精密工程, 2014, 22(4): 1048-1055. doi: 10.3788/OPE.20142204.1048.

ZHAO Hongyu, XIAO Chuangbo, and YU Jing. A Retinex algorithm for night color image enhancement by MRF[J]. Optics and Precision Engineering, 2014, 22(4): 1048-1055. doi: 10.3788/OPE.20142204.1048.

NARASIMHAN S G and NAYAR S K. Chromatic frame-work for vision in bad weather[C]. IEEE Conference on Computer Vision Pattern Recognition, South Carolina, USA, 2000, 1: 598-605.

NARASIMHAN S G and NAYAR S K. Interactive (de) weathering of an image using physical models[C]. IEEE Workshop on Color and Photometric Methods in Computer Vision, Nice, France, 2003: 1-8.

TAN R T. Visibility in bad weather from a single image[C]. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 2008: 1-8. doi: 10.1109/ CVPR.2008.4587643.

FATTAL R. Single image dehazing[J]. ACM Transactions on Graphics, 2008, 27(3): 1-9. doi: 10.1145/1360612.1360671.

HE K, SUN J, and 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.

余淼, 胡占义. 高阶马尔科夫随机场及其在场景理解中的应用[J]. 自动化学报, 2015, 41(7): 1213-1234. doi: 10.16383/ j.aas.2015.c140684.

YU Miao and HU Zhanyi. Higher-order Markov random fields and their applications in scene understanding[J]. Acta Automatica Sinica, 2015, 41(7): 1213-1234. doi: 10.16383/ j.aas.2015.c140684.

FATTAL R. Dehazing using color-lines[J]. ACM Transactions on Graphics, 2014, 34(1): 1-14. doi: 10.1145/2651362.

CHAO S and TSAI D. An improved anisotropic diffusion model for detail and edges-preserving smoothing[J]. Pattern Recognition Letters, 2010, 31(13): 2012-2023. doi: 10.1016/ j.patrec.2010.06.004.

ANDREW B, PUSHMEET K, and Carsten R. Markov Random Fields for Vision and Image Processing[M]. Massachusetts London, England, The MIT Press, Cambridge, 2011: 311-328.

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