Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)  2018, Vol. 52 Issue (7): 1302-1309    DOI: 10.3785/j.issn.1008-973X.2018.07.010
自动化技术     
基于引导滤波和暗原色先验理论透射率估值的视频去雾算法
覃宏超, 李炎炎, 龙伟, 赵瑞朋
四川大学 制造科学与工程学院, 四川 成都 610065
Real-time video dehazing using guided filtering and transmissivity estimated based on dark channel prior theory
QIN Hong-chao, LI Yan-yan, LONG Wei, ZHAO Rui-peng
College of Manufacturing Science and Engineering, Sichuan University, Chengdu 610065, China
 全文: PDF(10595 KB)   HTML
摘要：

针对雾天条件下户外采集的图像严重退化问题，解决传统的暗原色先验理论算法出现的边缘残雾、天空区域去雾效果欠佳、实时性差和鲁棒性差等问题，提出去雾效果显著的实时视频去雾算法.对大气光散射模型进行改进，以引导滤波后的灰度图作为大气光估计图；利用四叉树法和暗原色先验理论（DCP）在暗原色图中寻找浓雾区域，求得透射率估计值；利用改进的大气光散射模型复原图像.通过大量实验表明，复原出的图像去雾效果彻底，色彩鲜艳亮丽，天空区域不会出现彩色失真，景深变化大的地方不会出现白边现象，对于不同浓度的雾都有着较好的去雾效果，处理速度快且稳定，适合于实时视频去雾.
Abstract:

An effective real-time video dehazing algorithm was proposed in view of serious degradation of outdoor images taken in foggy weather in order to overcome traditional dark channel prior algorithm's problems of remnant fog in edge, poor effect in the sky area and poor real-time and robustness. The algorithm modified the atmospheric light scattering model, and took gray-scale image after guided filtering as atmospheric light map. Then transmittance values of dense fog area were estimated by using the subdivision and dark channel prior theory algorithm on dark channel map. The image was restored by modified atmospheric light scattering model. Numerous experimental results show that the foggy image is clear and bright, and it won't appear any color distortion in sky area and white border in where depth of field change fast. A great dehazing effect can be achieved in pictures taken in different concentrations of fog. The algorithm is fast and stable, which is suitable for real-time video dehazing.
收稿日期: 2017-05-17 出版日期: 2018-06-26
CLC:  TN391  
基金资助:

四川省科技支撑计划资助项目（2014KJT070，2010GZ171）.
通讯作者: 李炎炎,女,博士,讲师.orcid.org/0000-0002-4449-4773.     E-mail: lyy_scu@163.com
作者简介: 覃宏超(1991-),男,硕士生,从事交通车辆智能技术的研究.orcid.org/0000-0002-1289-0683.E-mail:1210019130@qq.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　

引用本文:

覃宏超, 李炎炎, 龙伟, 赵瑞朋. 基于引导滤波和暗原色先验理论透射率估值的视频去雾算法[J]. 浙江大学学报(工学版), 2018, 52(7): 1302-1309.

QIN Hong-chao, LI Yan-yan, LONG Wei, ZHAO Rui-peng. Real-time video dehazing using guided filtering and transmissivity estimated based on dark channel prior theory. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(7): 1302-1309.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2018.07.010    或    http://www.zjujournals.com/eng/CN/Y2018/V52/I7/1302

[1] 王多超,王永国,董雪梅,等.贝叶斯框架下的单幅图像去雾算法[J].计算机辅助设计与图形学学报,2010,22(10):1756-1761. WANG Duo-chao, WANG Yong-guo, DONG Xue-mei, et al. Single image dehazing based on Bayesian framework[J]. Journal of Computer-Aided Design and Computer Graphics, 2010, 22(10):1756-1761.
[2] 薛模根,周浦城,张洪坤.利用方向延伸专家场的单幅雾天图像复原[J].计算机辅助设计与图形学学报,2014,26(05):782-787. XUE Mo-gen, ZHOU Pu-cheng, ZHANG Hong-kun. Single foggy image restoration using orientation extended field of experts[J]. Journal of Computer-Aided Design and Computer Graphics, 2014, 26(05):782-787.
[3] 陆健强,王卫星,胡子昂,等.基于改进暗通道先验算法的农田视频实时去雾清晰化系统[J].农业工程学报,2016,32(10):143-148. LU Jian-qiang, WANG Wei-xing, HU Zi-ang, et al. Real time defogging system used for video image of farmland based on modified dark channel prior algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(10):143-148.
[4] 张万绪,袁永德,闫阳,等.基于暗原色先验的快速视频去雾优化算法[J].西北大学学报:自然科学版,2016,46(01):43-47. ZHANG Wan-xu, YUAN Yong-de, YAN Yang, et al. Fasting video haze removal algorithm using dark channel prior[J]. Journal of Northwest University:Natural Science Edition, 2016, 46(01):43-47.
[5] 吴迪,朱青松.图像去雾的最新研究进展[J].自动化学,2015,41(02):221-239. WU Di, ZHU Qing-song. The latest research progress of image dehazing. Acta Automatica Sinica, 2015,41(02):221-239.
[6] 郭璠,蔡自兴,谢斌,等.图像去雾技术研究综述与展望[J].计算机应用,2010,30(09):2417-2421. GUO Fan, CAI Zi-xing, XIE Bin, et al. Review and prospect of image dehazing techniques[J]. Journal of Computer Applications, 2010, 30(09):2417-2421.
[7] 刘茜,卢心红,李象霖.基于多尺度Retinex的自适应图像增强方法[J].计算机应用,2009,29(08):2077-2079. LIU Qian, LU Xin-hong, LI Xiang-lin. Adaptive image enhancement method based on multi-scale Retinex algorithm[J]. Journal of Computer Applications, 2009,29(08):2077-2079.
[8] 杨万挺,汪荣贵,方帅,等.滤波器可变的Retinex雾天图像增强算法[J].计算机辅助设计与图形学学报,2010,22(06):965-971. YANG Wan-ting, WANG Rong-gui, FANG Shuai, et al. Variable filter Retinex algorithm for foggy image enhancement[J]. Journal of Computer-Aided Design and Computer Graphics, 2010, 22(06):965-971.
[9] 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(07):1264-1273. LIU Hai-bo, YANG Jie, WU Zheng-ping, et al. A fast single image dehazing method based on dark channel prior and Retinex theory[J]. Acta Automatica Sinica, 2015, 41(07):1264-1273.
[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.
[11] NARASIHAM S G, NAYAR S K. Vision and the atmosphere[J]. International Journal of Computer Vision, 2002, 48(3):233-254.
[12] OAKLEY J P, SATHERLEY B L. Improving image quality in poor visibility conditions using a physical model for contrast degradation[J]. IEEE Transaction on Image Processing, 1998, 7(2):167-179.
[13] HE K, SUN J, TANG X. Guide image filtering[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence, 2013, 35(6):1397-1409.
[14] KIM J, JANG W, SIM J, et al. Optimized contrast enhancement for real-time image and video dehazing[J]. Journal of Visual Communication and Image Presentation, 2013, 24(3):410-425.
[15] 曾浩,尚媛园,丁辉,等.基于暗原色先验的图像快速去雾[J].中国图象图形学报,2015,20(07):914-921. ZENG Hao, SHANG Yuan-yuan, DING Hui, et al. Fast image haze removal base on dark channel prior[J]. Journal of Image and Graphics, 2015, 20(07):914-921.
[16] 郭璠,蔡自兴.图像去雾算法清晰化效果客观评价方法[J].自动化学报,2012,38(09):1410-1419. GUO Fan, CAI Zi-xing. Objective assessment method for the clearness effect of image defogging algorithm[J]. Acta Automatica Sinica, 2012, 38(09):1410-1419.
[17] 李大鹏,禹晶,肖创柏.图像去雾的无参考客观质量评测方法[J].中国图象图形学报,2011,16(09):1753-1757. LI Da-peng, YU Jing, XIAO Chuang-bai. No-referencequality assessment method for defogged images[J]. Journal of Image and Graphics, 2011, 16(09):1753-1757.
[18] 刘海波,汤群芳,杨杰.改进直方图均衡和Retinex算法在灰度图像增强中的应用[J].量子电子学报,2014,31(05):525-532. LIU Hai-bo, TANG Qun-fang, YANG Jie. Application of improved histogram equalization and Retinex algorithm in gray image enhancement[J]. Chinese Journal of Quantun Electronics, 2014, 31(05):525-532.
No related articles found!