基于结构化森林的前向车载视频图像的细节强化

doi:10.16182/j.issn1004731x.joss.201812014

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (12): 4602-4610.doi: 10.16182/j.issn1004731x.joss.201812014

基于结构化森林的前向车载视频图像的细节强化

金炜东¹, 胡燕花², 唐鹏¹, 李伟¹

1.西南交通大学,成都 610031;
2.成都地铁运营有限公司,成都 610081

收稿日期:2018-05-10 修回日期:2018-07-29 出版日期:2018-12-10 发布日期:2019-01-03
作者简介:金炜东(1959-),男,安徽,博士,教授,博导,研究方向为智能信息处理、模式识别等; 胡燕花(1991-),女,河南商丘,硕士生,助理工程师,研究方向为AFC。
基金资助:
国家自然科学基金重点项目(61134002), 中央高校基本科研业务费创新(2682014CX027), 国家重点研发计划(2016YFB1200401-102F)

Detailed Enhancement of Forward Vehicle Video Images Based on Structured Forest

Jin Weidong¹, Hu Yanhua², Tang Peng¹, Li Wei¹

1.Southwest Jiao tong University, Chengdu 610031, China;
2.Chengdu Metro Operation Co., Ltd. Chengdu 610081, China

Received:2018-05-10 Revised:2018-07-29 Online:2018-12-10 Published:2019-01-03

摘要/Abstract

摘要： 针对现有图像增强方法复杂度高,不能突出检测目标的问题,提出了一种基于结构化森林的前向车载视频图像细节强化的方法。该方法主要分为两个部分:结构化森林边缘检测和视觉增强。实现了接触网巡检图像的细节边缘强化,处理后的灰度直方图分布更均衡,增强后的巡检图像的细节区域与背景区域的灰度平均值之差、标准差之差更大,峰值噪声比以及结构相似性获得了提高,通过与其他方法对比,表明算法行之有效。可以更加直观的为铁路工作人员展示铁路基础设施的异常情况,具有很强的实践意义。

关键词: 结构化森林, 边缘检测, 图像增强, 图像融合

Abstract: In view of the complexity of existing image enhancement methods, which cannot highlight the detection target, this paper proposes a method for detail enhancement of forward vehicle video based on structured forest. This method is mainly divided into two parts: structured forest edge detection and visual enhancement. This paper implements the detail edge extraction and enhancement of the patrolling image of the catenary. The gray histogram distribution after processing is more balanced, and the difference between the average values of gray levels in the detail area and background area of the enhanced patrol image is the standard deviation. The difference is even greater, the peak-to-noise ratio and the structural similarity have been improved. By comparing with other methods, it shows that the algorithm is effective. It can be more intuitive to show railroad workers the anomalous conditions of the railway infrastructure, which is of great practical significance.

Key words: structured forest, edge detection, image enhancement, image fusion

中图分类号:

金炜东, 胡燕花, 唐鹏, 李伟. 基于结构化森林的前向车载视频图像的细节强化[J]. 系统仿真学报, 2018, 30(12): 4602-4610.

Jin Weidong, Hu Yanhua, Tang Peng, Li Wei. Detailed Enhancement of Forward Vehicle Video Images Based on Structured Forest[J]. Journal of System Simulation, 2018, 30(12): 4602-4610.

参考文献

[1] 肖晓. 基于Retinex的图像增强算法研究与实现[D]. 成都: 四川师范大学, 2016.
XIAO Xiao.Research and Implementation of Image Enhancement Algorithm Based on Retinex[D]. Chengdu: Sichuan Normal University, 2016.
[2] 刘洋, 潘静, 庞彦伟. 一种基于暗原色先验的低照度视频增强算法[J]. 电子测量技术, 2013, 36(10): 55-57, 77.
Liu Yang, Pan Jing, Pang Yanwei.A low-light video enhancement algorithm based on dark color priors[J]. Electronic Measurement Technology, 2013, 36(10): 55-57, 77.
[3] 扈佃海, 吕绪良, 文刘强. 一种改进的直方图均衡化图像增强方法[J]. 光电技术应用, 2012, 27(3): 65-68.
Hu Dianhai, Lű Xuliang, Wen Liuqiang.An improved image enhancement method for histogram equalization[J]. Optoelectronics Technology Application, 2012, 27(3): 65-68.
[4] 方明, 李洪娜, 雷立宏, 等. 低照度视频图像增强算法综述[J]. 长春理工大学学报(自然科学版), 2016, 39(3): 56-64,69.
Fang Ming, Li Hongna, Lei Lihong, et al.Review of low illumination video image enhancement algorithms[J]. Journal of Changchun University of Science and Technology (Natural Science), 2016, 39(3): 56-64, 69.
[5] Yanhua H, Peng T, Weidong J, et al.The image enhancement of forward vehicle video based on catenary semantics[C]. Electronic Measurement & Instruments (ICEMI), 2017 13th IEEE International Conference on. IEEE, 2017: 477-482.
[6] Dollár P, Zitnick C L.Fast Edge Detection Using Structured Forests[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 2014, 37(8): 1558-70.
[7] Li Y, Brown M S.Single image layer separation using relative smoothness[C]//. IEEE Conference on Computer Vision and Pattern Recognition. USA: IEEE Computer Society, 2014: 2752-2759.
[8] Martin D, Fowlkes C, Tal D, et al.A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics[C]//. Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on. USA: IEEE, 2002, 2: 416-423.
[9] Canny J.A Computational Approach to Edge Detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 1986, 8(6): 679-698.
[10] 易倩. 基于交通视频图像处理的车型识别技术的研究[D]. 广州: 华南理工大学, 2016.
YI Qian.Research on vehicle identification technology based on traffic video image processing[D]. Guangzhou: South China University of Technology, 2016.
[11] 王培珍, 毛雪芹, 毛雪菲, 等. 基于均值偏移和边缘置信度的焦炭显微图像分割[J]. 中国图象图形学报, 2010, 15(10): 1478-1484.
Wang Peizhen, Mao Xueqin, Mao Xuefei, et al.Coke Microscopy Image Segmentation Based on Mean Shift and Edge Confidence[J]. Journal of Image and Graphics, 2010, 15(10): 1478-1484.
[12] R A Hummel.Image Enhancement by Histogram Transformation[J]. Computer Graphics and Image Processing (S0146-664X), 1977, 6(2): 184-195.
[13] S M Pizer.Adaptive Histogram Equalization and its variation[J]. Computer Vision graphics and Image Processing (S0734-189X), 1987, 39(3): 355-368.
[14] T Stathaki.Image Fusion Algorithms and Applications [M]. Academic Press is an imprint of Elsevier, 2008.
[15] Yang X, Zhang R.New image quality assessment method using wavelet leader pyramids[J]. Optical Engineering (S0091-3286), 2011, 50(6): 067011-8.
[16] 杨卫中, 徐银丽, 乔曦, 等. 基于对比度受限直方图均衡化的水下海参图像增强方法[J]. 农业工程学报, 2016, 32(6): 197-203.
Yang Weizhong, Xu Yinli, Qiao Xi, et al.Image enhancement method of underwater sea cucumber based on contrast-constrained histogram equalization[J]. Journal of Agricultural Engineering, 2016, 32(6): 197-203.
[17] He K, Sun J, Tang X.Single Image Haze Removal Using Dark Channel Prior[J]. IEEE Trans Pattern Anal Mach Intell (S0162-8828), 2011, 33(12): 2341-2353.

基于结构化森林的前向车载视频图像的细节强化

Detailed Enhancement of Forward Vehicle Video Images Based on Structured Forest

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 10

编辑推荐

Metrics

本文评价

[1]	周培培, 侯幸林. 一种用于图像融合的无监督深度神经网络[J]. 系统仿真学报, 2022, 34(6): 1267-1274.
[2]	沈祺宗, 高春艳. 融合边缘检测模块的自然地貌语义分割模型研究[J]. 系统仿真学报, 2022, 34(2): 293-302.
[3]	蔡兴泉, 杨哲, 何鑫, 陈超. 结合伺服电机的古建筑物增强现实交互展示系统[J]. 系统仿真学报, 2020, 32(6): 1145-1154.
[4]	王依人, 邓国庆, 夏营威, 张龙, 刘勇, 张文. 基于方向可调滤波器的血管图像增强算法[J]. 系统仿真学报, 2018, 30(6): 2095-2101.
[5]	李海洋, 曹伟国, 李诗锐, 陶克路, 李华. 渐进式图像去噪算法[J]. 系统仿真学报, 2017, 29(2): 282-294.
[6]	孙逊, 王玄玉, 王献, 刘顺华. 实时修正的烟幕随机游走仿真模型[J]. 系统仿真学报, 2016, 28(9): 1979-1984.
[7]	赵毅力, 徐丹, 钱文华, 张雁. 基于种子区域增长的快速图像融合[J]. 系统仿真学报, 2016, 28(9): 1985-1989.
[8]	姚莉, 吴琼颖, 许辉. 基于网格映射的虚拟视点合成[J]. 系统仿真学报, 2016, 28(10): 2321-2328.
[9]	沈伟华, 王盛波, 潘志庚. 基于互信息和图像融合的二维流场可视化[J]. 系统仿真学报, 2015, 27(8): 1796-1800.
[10]	温凯峰, 李秉键. 基于多尺度系数分解框架的医学图像融合[J]. 系统仿真学报, 2015, 27(10): 2615-2621.