红外与激光工程, 2017, 46 (8): 0818005, 网络出版: 2017-11-07   

红外与可见光图像融合的汽车抗晕光系统

Vehicles anti-halation system based on infrared and visible images fusion
作者单位
西安工业大学 电子信息工程学院, 陕西 西安 710021
摘要
针对夜间汽车晕光现象引起的交通安全问题, 从规避碰撞物的角度出发, 设计了一种红外与可见光图像融合的视频抗晕光系统。系统通过对可见光图像和红外图像做MSR图像增强, 解决了夜间可见光图像亮度低, 暗处信息不易获取的问题, 并提高了红外图像对比度, 提升了融合图像的清晰度; 通过YUV与小波变换结合的方式对增强后的可见光图像和红外图像进行融合, 消除了晕光现象。实验结果的主客观分析表明: 该融合算法比YUV与小波融合算法在熵、均值、平均梯度、标准差上分别提高了1.6%、13.5%、25.3%、0.6%, 该系统不仅能有效消除晕光, 还对融合后图像的亮度和暗处细节信息有较大提升, 提高了夜间驾驶安全性。
Abstract
To avoid collisions caused by night vehicles halation, a video anti-halation system of infrared and visible images fusion was designed. Visible light image and infrared image were enhanced by MSR enhancement algorithm to solve the difficulty in achieving the dark place information caused by low-light level of night visible light image, and the contrast of infrared image was improved, which consequently improved the definition of the fusion image; And the halation was eliminated by the method of infrared and visible images fusion based on YUV and wavelet transformation. Compared with the YUV and wavelet fusion algorithm, the fusion algorithm proposed in this paper can increase the entropy, mean value, average gradient and standard deviation by 1.6%, 13.5%, 25.3% and 0.6%, respectively. Experiment results show that the designed system can effectively eliminate the halation, and improves the image brightness and details in the picture, which improves the safety of night driving.
参考文献

[1] 陈琳. 夜间行车远光灯会车法律问题研究[J]. 法制与社会, 2014(23): 71-72.

    Chen Lin. The legal issues study on the high beams car meeting at night[J]. Legal System and Soceity, 2014(23): 71-72. (in Chinese)

[2] 骆媛, 王岭雪, 金伟其, 等. 微光(可见光)/红外彩色夜视技术处理算法及系统进展[J]. 红外技术, 2010, 32(6): 337-344.

    Luo Yuan, Wang Lingxue, Jin Weiqi, et al. Developments of image processing algorithms and systems for LLL(Vis.)/IR color night vision[J]. Infrared Technology, 2010, 32(6): 337-344. (in Chinese)

[3] Wang Jian, Yang Zhe, Guo Quanmin. Research on video anti-blooming[C]//International Congress on Image and Signal Processing, 2010, 3: 491-494.

[4] 郭全民, 李晓玲. 基于可见光和红外图像融合的汽车抗晕光方法[J]. 红外技术, 2015, 37(6): 475-478.

    Guo Quanmin, Li Xiaoling. Vehicles′ anti-blooming method based on visible and infrared images fusion[J]. Infrared Technology, 2015, 37(6): 475-478. (in Chinese)

[5] 王健, 郑少峰. 基于YUV与小波变换的可见光与红外图像融合[J]. 西安工业大学学报, 2013, 33(3): 209-211.

    Wang Jian, Zheng Shaofeng. Visible and infrared image fusion based on YUV color space and wavelet transform[J]. Journal of Xi′an Technological University, 2013, 33(3): 209-211. (in Chinese)

[6] 窦易文, 周鸣争, 唐肝翌, 等. 焦点引导的带颜色恢复的多尺度Retinex算法[J]. 计算机工程与应用, 2013, 49(2): 207-270.

    Dou Yiwen, Zhou Mingzheng, Tang Ganyi, et al. Focus-guided multi-scale Retinex with color restore algorithm[J].Computer Engineering and Applications, 2013, 49(2): 207-270. (in Chinese)

[7] 黄永东, 摆晓娟, 杨建伟. 基于中心投影的仿射变换参数估计与实现[J]. 光电子·激光, 2013, 24(9): 1833-1837.

    Huang Yongdong, Bai Xiaojuan, Yang Jianwei. Parametric estimation and realization of affine transformation based on the central projection[J]. Journal of Optoelectronics·Laser,2013, 24 (9): 1833-1837. (in Chinese)

[8] Jiang B, Woodell G A, Jobson D J. Novel multi-scale retinex with color restoration on graphics processingunit[J]. Journal of Real-Time Image Processing, 2015, 10(2): 239- 253.

[9] Liang J W, Zhang X Q. Retinex by higher order total variation decomposition[J]. Journal of Mathematical Imaging & Vision, 2015, 52(3): 345-355.

[10] 陈志斌, 张超, 宋岩, 等. 灰度拉伸Retinex在大动态范围烟雾图像增强中的应用[J]. 红外与激光工程, 2014, 43(9): 3146-3150.

    Chen Zhibin, Zhang Chao, Song Yan, et al. Application of Retinex with grayscale stretching in large dynamic range smoke image enhancement[J]. Infrared and Laser Engineering, 2014, 43(9): 3146-3150. (in Chinese)

[11] 李锦, 王俊平, 万国挺, 等. 一种结合直方图均衡化和MSRCR的图像增强新算[J]. 西安电子科技大学学报, 2014, 41(3): 104-109.

    Li Jin, Wang Junping, Wan Guoting, et al. Novel algorithm for image enhacement with histogram equalization and MSRCR[J]. Journal of Xidian University, 2014, 41(3): 104-109. (in Chinese)

[12] 曾祥通, 张玉珍, 孙佳嵩, 等. 颜色对比度增强的红外与可见光图像融合方法[J].红外与激光工程, 2015, 44(4): 1198-1202.

    Zeng Xiangtong, Zhang Yuzhen, Sun Jiasong, et al. One color contrast enhaced infrared and visible image fusion method[J]. Infrared and Laser Engineering, 2015, 44(4): 1198-1202. (in Chinese)

[13] 杨少魁, 刘文. 一种微光与红外图像彩色融合方法[J]. 红外与激光工程, 2014, 43(5): 1654-1659.

    Yang Shaokui, Liu Wen. Color fusion method for low-level light and infrared inages[J]. Infrared and Laser Engineering, 2014, 43(5): 1654-1659. (in Chinese)

[14] 童涛, 孟强强, 孙嘉成, 等. 基于边缘特征的多传感器图像融合算法[J]. 红外与与激光工程, 2014, 43(1): 311-317.

    Tong Tao, Meng Qiangqiang, Sun Jiacheng, et al. Multi-sensor image fusion algorithm based on edge feature[J]. Infrared and Laser Engineering, 2014, 43(1): 311-317. (in Chinese)

[15] 徐春梅, 李刚, 胡文

    Xu Chunmei, Li Gang, Hu Wengang, et al. Infrared image quality evaluation[J]. Infrared Technology, 2004, 26(6): 72-75. (in Chinese)

[16] 陈惠娟, 钱亚枫, 李勃, 等. 基于HVS和四元数的彩色图像质量评价方法[J]. 南京大学学报, 2015, 51(2): 271-278.

    Chen Huijuan, Qian Yafeng, Li Bo, et al. Assessment method for color image quality based on HVS and quaternion[J]. Jounal of Nanjing University, 2015, 51(2): 271-278. (in Chinese)

郭全民, 董亮, 李代娣. 红外与可见光图像融合的汽车抗晕光系统[J]. 红外与激光工程, 2017, 46(8): 0818005. Guo Quanmin, Dong Liang, Li Daidi. Vehicles anti-halation system based on infrared and visible images fusion[J]. Infrared and Laser Engineering, 2017, 46(8): 0818005.

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