留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

结合暗原色优先和Gamma校正的红外交通图像增强算法

顾明 郑林涛 刘中华

顾明, 郑林涛, 刘中华. 结合暗原色优先和Gamma校正的红外交通图像增强算法[J]. 交通运输工程学报, 2016, 16(6): 149-158.
引用本文: 顾明, 郑林涛, 刘中华. 结合暗原色优先和Gamma校正的红外交通图像增强算法[J]. 交通运输工程学报, 2016, 16(6): 149-158.
GU Ming, ZHENG Lin-tao, LIU Zhong-hua. Infrared traffic image's enhancement algorithm combining dark channel prior and Gamma correction[J]. Journal of Traffic and Transportation Engineering, 2016, 16(6): 149-158.
Citation: GU Ming, ZHENG Lin-tao, LIU Zhong-hua. Infrared traffic image's enhancement algorithm combining dark channel prior and Gamma correction[J]. Journal of Traffic and Transportation Engineering, 2016, 16(6): 149-158.

结合暗原色优先和Gamma校正的红外交通图像增强算法

基金项目: 

国家自然科学基金项目 61377012

详细信息
    作者简介:

    顾明(1983-), 男, 河南宁陵人, 清华大学工学博士研究生, 从事图像分析处理研究

  • 中图分类号: U491.1

Infrared traffic image's enhancement algorithm combining dark channel prior and Gamma correction

More Information
  • 摘要: 为了有效提高智能交通监控设备采集到的红外交通图像的视觉质量, 将可见光图像去雾的方法引入红外交通图像的增强处理过程中, 提出一种结合暗原色优先和Gamma校正的红外交通图像增强新算法, 首先采用暗原色优先算法对原始降质红外交通图像进行处理而得到初步增强的图像, 然后使用Gamma校正算法对初步增强的图像亮度进行调节, 并将新算法与其他常见的红外图像增强算法进行图像增强效果的对比分析。试验结果表明: 两幅原始红外交通图像的信息熵分别为4.71、5.07, 经过新算法处理后信息熵分别增加到6.45、5.92;两幅原始红外交通图像的灰度标准差分别为6.90、19.14, 经过新算法处理后灰度标准差分别增加到31.17、32.35;新算法的信息熵计算值大于他他算法的计算值。可见, 新算法的增强效果优于其他常见的红外图像增强算法, 它能显著改善红外交通图像的视觉效果, 为图像的后续处理与分析奠定良好的基础。

     

  • 图  1  原始红外交通图像

    Figure  1.  Original infrared traffic image

    图  2  经暗原色优先算法处理后的图像

    Figure  2.  Image after processed by dark channel prior algorithm

    图  3  算法流程

    Figure  3.  Algorithm flowchart

    图  4  原始红外交通图像1

    Figure  4.  Original infrared traffic image 1

    图  5  算法1处理后的图像1

    Figure  5.  Image 1processed by algorithm 1

    图  6  算法2处理后的图像1

    Figure  6.  Image 1processed by algorithm 2

    图  7  算法3处理后的图像1

    Figure  7.  Image 1processed by algorithm 3

    图  8  算法4处理后的图像1

    Figure  8.  Image 1processed by algorithm 4

    图  9  算法5处理后的图像1

    Figure  9.  Image 1processed by algorithm 5

    图  10  原始红外交通图像1的灰度直方图

    Figure  10.  Gray scale histogram of original infrared traffic image 1

    图  11  算法1处理后的图像1的灰度直方图

    Figure  11.  Gray scale histogram of image 1processed by algorithm 1

    图  12  算法2处理后的图像1的灰度直方图

    Figure  12.  Gray scale histogram of image 1processed by algorithm 2

    图  13  算法3处理后的图像1的灰度直方图

    Figure  13.  Gray scale histogram of image 1processed by algorithm 3

    图  14  算法4处理后的图像1的灰度直方图

    Figure  14.  Gray scale histogram of image 1processed by algorithm 4

    图  15  算法5处理后的图像1的灰度直方图

    Figure  15.  Gray scale histogram of image 1processed by algorithm 5

    图  16  原始红外交通图像2

    Figure  16.  Original infrared traffic image 2

    图  17  算法1处理后的图像2

    Figure  17.  Image 2processed by algorithm 1

    图  18  算法2处理后的图像2

    Figure  18.  Image 2processed by algorithm 2

    图  19  算法3处理后的图像2

    Figure  19.  Image 2processed by algorithm 3

    图  20  算法4处理后的图像2

    Figure  20.  Image 2processed by algorithm 4

    图  21  算法5处理后的图像2

    Figure  21.  Image 2processed by algorithm 5

    图  22  原始红外交通图像2的灰度直方图

    Figure  22.  Gray scale histogram of original infrared traffic image 2

    图  23  算法1处理后的图像2的灰度直方图

    Figure  23.  Gray scale histogram of image 2processed by algorithm 1

    图  24  算法2处理后的图像2的灰度直方图

    Figure  24.  Gray scale histogram of image 2processed by algorithm 2

    图  25  算法3处理后的图像2的灰度直方图

    Figure  25.  Gray scale histogram of image 2processed by algorithm 3

    图  26  算法4处理后的图像2的灰度直方图

    Figure  26.  Gray scale histogram of image 2processed by algorithm 4

    图  27  算法5处理后的图像2的灰度直方图

    Figure  27.  Gray scale histogram of image 2processed by algorithm 5

    表  1  图像1的定量指标比较

    Table  1.   Comparison of quantitative indexes for image 1

    下载: 导出CSV

    表  2  图像2的定量指标比较

    Table  2.   Comparison of quantitative indexes for image 2

    下载: 导出CSV
  • [1] 白立岗, 贾冬冬. 红外摄像机在交通监控系统中的应用[J]. 中国交通信息产业, 2009(11): 89-90. doi: 10.3969/j.issn.1672-3333.2009.11.017

    BAI Li-gang, JIA Dong-dong. Application of infrared camera in traffic monitoring system[J]. China ITS Journal, 2009(11): 89-90. (in Chinese). doi: 10.3969/j.issn.1672-3333.2009.11.017
    [2] 杜豫川, 张晓明, 刘成龙, 等. 基于红外图像和普通图像对比的高速公路可视度分析[J]. 交通运输系统工程与信息, 2016, 16(4): 73-78. doi: 10.3969/j.issn.1009-6744.2016.04.011

    DU Yu-chuan, ZHANG Xiao-ming, LIU Cheng-long, et al. Visibility analysis for freeway based on comparison of ordinary and infrared images[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(4): 73-78. (in Chinese). doi: 10.3969/j.issn.1009-6744.2016.04.011
    [3] 丁利伟, 王宗俐, 程明阳. 高速公路红外引导系统透雾特性的试验研究[J]. 光电技术应用, 2014, 29(2): 4-9, 21. doi: 10.3969/j.issn.1673-1255.2014.02.002

    DING Li-wei, WANG Zong-Li, CHENG Ming-yang. Test research on detection ability of infrared guidance system for freeway traffic in fog[J]. Electro-Optic Technology Application, 2014, 29(2): 4-9, 21. (in Chinese). doi: 10.3969/j.issn.1673-1255.2014.02.002
    [4] LIN C L. An approach to adaptive infrared image enhancement for long-range surveillance[J]. Infrared Physics and Technology, 2011, 54(2): 84-91. doi: 10.1016/j.infrared.2011.01.001
    [5] NI Chao, LI Qi, XIA L Z. A novel method of infrared image denoising and edge enhancement[J]. Signal Processing, 2008, 88(6): 1606-1614. doi: 10.1016/j.sigpro.2007.12.016
    [6] LIANG Kun, MA Yong, XIE Yue, et al. A new adaptive contrast enhancement algorithm for infrared images based on double plateaus histogram equalization[J]. Infrared Physics and Technology, 2012, 55(4): 309-315. doi: 10.1016/j.infrared.2012.03.004
    [7] BAI Xiang-zhi, ZHOU Fu-gen, XUE Bin-dang. Image enhancement using multi scale image features extracted by top-hat transform[J]. Optics and Laser Technology, 2012, 44(2): 328-336. doi: 10.1016/j.optlastec.2011.07.009
    [8] LAI Rui, YANG Yin-tang, WANG Bing-jian, et al. A quantitative measure based infrared image enhancement algorithm using plateau histogram[J]. Optics Communications, 2010, 283(21): 4283-4288. doi: 10.1016/j.optcom.2010.06.072
    [9] DAI Shao-sheng, LIU Qin, LI Peng-fei, et al. Study on infrared image detail enhancement algorithm based on adaptive lateral inhibition network[J]. Infrared Physics and Technology, 2015, 68: 10-14. doi: 10.1016/j.infrared.2014.09.042
    [10] YUAN L T, SWEE S K, PING T C. Infrared image enhancement using adaptive trilateral contrast enhancement[J]. Pattern Recognition Letters, 2015, 54: 103-108. doi: 10.1016/j.patrec.2014.09.011
    [11] ZHAO Ju-feng, CHEN Yue-ting, FENG Hua-jun, et al. Infrared image enhancement through saliency feature analysis based on multi-scale decomposition[J]. Infrared Physics and Technology, 2014, 62: 86-93. doi: 10.1016/j.infrared.2013.11.008
    [12] BAI X Z, ZHOU F G. Top-hat selection transformation for infrared dim small target enhancement[J]. The Imaging Science Journal, 2010, 58(2): 112-117. doi: 10.1179/136821909X12581187860176
    [13] ZUO Chao, CHEN Qian, LIU Ning, et al. Display and detail enhancement for high-dynamic-range infrared images[J]. Optical Engineering, 2011, 50(12): 1-9.
    [14] LIU Ning, ZHAO Dong-xue. Detail enhancement for highdynamic-range infrared images based on guided image filter[J]. Infrared Physics and Technology, 2014, 67: 138-147. doi: 10.1016/j.infrared.2014.07.013
    [15] ZHAO Wen-da, XU Zhi-jun, ZHAO Jian, et al. Infrared image detail enhancement based on the gradient field specification[J]. Applied Optics, 2014, 53(19): 4141-4149. doi: 10.1364/AO.53.004141
    [16] ZHAO Ju-feng, CHEN Yue-ting, FENG Hua-jun, et al. Fast image enhancement using multi-scale saliency extraction in infrared imagery[J]. Optik, 2014, 125(15): 4039-4042. doi: 10.1016/j.ijleo.2014.01.117
    [17] VICKERS V E. Plateau equalization algorithm for real-time display of high-quality infrared imagery[J]. Optical Engineering, 1996, 35(7): 1921-1926. doi: 10.1117/1.601006
    [18] WANG Bing-jian, LIU Shang-qian, LI Qing, et al. A realtime contrast enhancement algorithm for infrared images based on plateau histogram[J]. Infrared Physics and Technology, 2006, 48(1): 77-82. doi: 10.1016/j.infrared.2005.04.008
    [19] HE Kai-ming, SUN Jian, Tang Xiao-ou. 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
    [20] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201402018.htm

    ZHOU Yu-wei, CHEN Qiang, SUN Quan-sen, 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). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201402018.htm
    [21] HUANG S C, CHENG F C, CHIU Y S. Efficient contrast enhancement using adaptive Gamma correction with weighting distribution[J]. IEEE Transactions on Image Processing, 2013, 22(3): 1032-1041. doi: 10.1109/TIP.2012.2226047
    [22] DENG Guang. A generalized gamma correction algorithm based on the SLIP model[J]. EURASIP Journal on Advances in Signal Processing, 2016, 2016(1): 1-15. doi: 10.1186/s13634-015-0293-z
    [23] RAHMAN S, RAHMAN M M, ABDULLAH-AL-WADUD M, et al. An adaptive gamma correction for image enhancement[J]. EURASIP Journal on Image and Video Processing, 2016, 2016(1): 1-13.
    [24] JIANG G, WONG C Y, LIN S C F, et al. Image contrast enhancement with brightness preservation using an optimal gamma correction and weighted sum approach[J]. Journal of Modern Optics, 2015, 62(7): 536-547. doi: 10.1080/09500340.2014.991358
    [25] GUPTA B, TIWARI M. Minimum mean brightness error contrast enhancement of color images using adaptive gamma correction with color preserving framework[J]. Optik, 2016, 127(4): 1671-1676. doi: 10.1016/j.ijleo.2015.10.068
    [26] 符富强. 基于NURBS曲线的GAMMA校正技术的研究与应用[D]. 西安: 西安电子科技大学, 2010.

    FU Fu-qiang. Research and application of GAMMA calibration technology based on NURBS curve[D]. Xi'an: Xidian University, 2010. (in Chinese).
    [27] 马琳, 王钧慧, 王宽全, 等. 基于特征图规正的虹膜图像自适应Gamma校正方法[J]. 燕山大学学报, 2010, 34(2): 173-179. https://www.cnki.com.cn/Article/CJFDTOTAL-DBZX201002019.htm

    MA Lin, WANG Jun-hui, WANG Kuan-quan, et al. Adaptive Gamma correction method of iris image based on characteristic pattern[J]. Journal of Yanshan University, 2010, 34(2): 173-179. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DBZX201002019.htm
    [28] 储清翠, 王华彬, 陶亮. 图像的局部自适应Gamma校正[J]. 计算机工程与应用, 2015, 51(7): 189-193, 208. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201507037.htm

    CHU Qing-cui, WANG Hua-bin, TAO Liang. Local adaptive Gamma correction method[J]. Computer Engineering and Applications, 2015, 51(7): 189-193, 208. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201507037.htm
    [29] 李渤, 朱梅, 樊中奎, 等. 非均匀光照图像自适应Gamma增强算法[J]. 南昌大学学报: 理科版, 2016, 40(3): 299-302. https://www.cnki.com.cn/Article/CJFDTOTAL-NCDL201603017.htm

    LI Bo, ZHU Mei, FAN Zhong-kui, et al. An adaptive gamma enhancement algorithm for non-uniform illumination images[J]. Journal of Nanchang University: Natural Science, 2016, 40(3): 299-302. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-NCDL201603017.htm
    [30] 张曙. 自然环境下交通标志的检测及识别算法研究[D]. 武汉: 武汉理工大学, 2014.

    ZHANG Shu. Detection and recognition algorithm research of traffic signs in natural environments[D]. Wuhan: Wuhan University of Technology, 2014. (in Chinese).
  • 加载中
图(27) / 表(2)
计量
  • 文章访问数:  3440
  • HTML全文浏览量:  136
  • PDF下载量:  2399
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-05-11
  • 刊出日期:  2016-12-25

目录

    /

    返回文章
    返回