[1]赵 谦,钱 渠,任志奇.BEMD分解的矿下图像增强算法[J].西安科技大学学报,2020,(03):484-491.[doi:10.13800/j.cnki.xakjdxxb.2020.0315]
 ZHAO Qian,QIAN Qu,REN Zhi-qi.Undermine image enhancement algorithm based on BEMD decomposition[J].Journal of Xi'an University of Science and Technology,2020,(03):484-491.[doi:10.13800/j.cnki.xakjdxxb.2020.0315]
点击复制

BEMD分解的矿下图像增强算法(/HTML)
分享到:

西安科技大学学报[ISSN:1672-9315/CN:61-1434/N]

卷:
期数:
2020年03期
页码:
484-491
栏目:
出版日期:
2020-05-15

文章信息/Info

Title:
Undermine image enhancement algorithm based on BEMD decomposition
文章编号:
1672-9315(2020)03-0484-08
作者:
赵 谦钱 渠任志奇
(西安科技大学 通信与信息工程学院,陕西 西安 710054)
Author(s):
ZHAO QianQIAN QuREN Zhi-qi
(College of Communication and Information Engineering,Xi'an University of Science and Technology,Xi'an 710054,China)
关键词:
图像增强 矿井图像 高通滤波 二维经验模态分解
Keywords:
image enhancement mine image high pass filtering BEMD
分类号:
TN 911.73
DOI:
10.13800/j.cnki.xakjdxxb.2020.0315
文献标志码:
A
摘要:
应用一种基于二维经验模态分解和高频滤波的图像增强算法,对处于恶劣环境中矿井下的图像进行增强处理,能有效解决此类图像存在边缘及纹理等局部细节信息模糊、对比度不高以及对噪声敏感等问题。首先,对输入的矿井下图像进行高通滤波处理,去除图像中的高频成分,得到矿井下图像的低频部分; 其次,用二维经验模态分解出图像的高频部分,弥补因第1步引起的图像细节信息丢失的不足; 再次,通过确定高低频比例因子c,将提取的高频细节与低频背景按3:2的比例融合,并有效抑制粉尘散射模糊和过曝光白色伪影现象的噪声; 最后,采用直方图均衡化来平衡图像灰度,增强图像的细节,提高图像整体的对比度。对比实验表明,在保证图像质量的前提下,所提算法与传统的高频强调滤波相比,处理后的图像清晰度Brenner指标提高10%,均方误差更小,在增强矿井下图像边缘纹理以及暗部细节效果明显,能够有效提高图像的对比度,增强图像的亮度和信息熵,能对后续的图像处理及分析提供有效的帮助。
Abstract:
Image enhancement algorithmbased on the two-dimensional empirical mode decomposition and high frequency filter,was applied to enhance the undermine images under bad conditions.The local details of such image,such asfuzzy edge and texture information,low contrast and high sensitivity of noise could be dealt with.Study is carried out by comparing it with the traditional image enhancement algorithms.First,high-pass filtering was carried out on the input image of underground mine,and the low-frequency part of the image was obtained by removing the high-frequency part of the image.Secondly,the high-frequency part of the image is decomposed by two-dimensional empirical mode to make up for the loss of image details caused by the first step.Thirdly,by determining the high and low frequency scaling factor c,the extracted high frequency details were fused with the low frequency background at a ratio of 3:2,and the noise of dust scattering blur and over-exposure white artifact was effectively suppressed.Finally,histogram equalization is used to balance the gray level of the image,enhance the details of the image,and improve the overall contrast of the image.Comparative experiments show that under the premise of guarantee the quality of images,this algorithm,compared with the traditional high frequency emphasis filtering,can improve the resolution of processed images by 10%,and the square error is small.Better effect has been achieved in dealing with edges in texture and shadow detail.This algorithm can effectively improve the contrast of image,enhance the brightness of the image and the information entropy,and will be helpfulin subsequent image processing and analysis.

参考文献/References:

[1] 李新锋.煤矿视频监控图像增强方法的研究[D].哈尔滨:黑龙江科技大学,2010. LI Xin-feng.Research on the enhancement method of coal mine video surveillance image[D].Harbin:Heilongjiang University of Science and Technology,2010. [2]王小元,张红英,吴亚东,等.基于物理模型的低照度图像增强算法[J].计算机应用,2015,35(8):2301-2304. WANG Xiao-yuan,ZHANG Hong-ying,WU Ya-dong,et al.Low illumination based on physical model image enhancement algorithm[J].Computer Application,2015,35(8):2301-2304. [3]Hao W,He M,Ge H,et al.Retinex-like method for image enhancement inpoor visibility conditions[J].Procedia Engineering,2011,15:2798-2803. [4]李益红,周晓谊.一种多分辨多尺度的Retinex彩色图像增强算法[J].计算机工程与应用,2017,53(16):193-198. LI Yi-hong,ZHOU Xiao-yi.A multi-resolution multi-scale Retinex color image enhancement algorithm[J].Computer Engineering and Applications,2017,53(16):193-198. [5]秦绪佳,王慧玲,杜轶诚,等,梁震华.HSV色彩空间的Retinex结构光图像增强算法[J].计算机辅助设计与图形学学报,2013,25(4):488-493. QIN Xu-jia,WANG Hui-ling,DU Yi-cheng,et al.Retinex structured light image enhancement algorithm for HSV color space[J].Journal of Computer-Aided Design & Computer Graphics,2013,25(4):488-493. [6]刘高平,赵 萌.基于亮度的自适应单尺度Retinex图像增强算法[J].光电工程,2011,38(2):71-77. LIU Gao-ping,ZHAO Meng.Adaptive single-scale Retinex image enhancement algorithm based on brightness[J].Optoelectronic Engineering,2011,38(2):71-77. [7]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(5):525-532. LIU Hai-bo,Tang Qun-fang,Yang Jie.Improved histogram equalization and Retinex algorithm in gray image enhancement[J].Journal of Quantum Electronics,2014,31(5):525-532. [8]胡韦伟,汪荣贵,方 帅,等.基于双边滤波的Retinex图像增强算法[J].工程图学学报,2010,31(2):104-109. HU Wei-wei,WANG Rong-gui,FANG Shuai,et al.Retinex image enhancement algorithm based on bilateral filtering[J].Journal of Engineering Graphics,2010,31(2):104-109. [9]李权合,毕笃彦,马时平,等.基于Retinex和视觉适应性的图像增强[J].中国图象图形学报,2010,15(12):1728-1732. LI Quan-he,BI Du-yan,MA Shi-ping,et al.Image enhancement based on Retinex and visual adaptation[J].Chinese Journal of Image and Graphics,2010,15(12):1728-1732. [10]Tang L,Chen S,Liu W,et al.Improved Retinex image enhancement algorithm[J].Procedia Environmentl Sciences,2011,11:208-212. [11]程德强,郑 珍,姜海龙.一种煤矿井下图像增强算法[J].工矿自动化,2015,41(12):31-34. CHENG De-qiang,ZHENG Zhen,JIANG Hai-long.A coal mine underground image enhancement algorithm[J].Industrial and Mining Automation,2015,41(12):31-34. [12]何 文.基于高频强调滤波的医学X光图像增强算法[J].信息技术,2015(4):60-62,66. HE Wen.Medical X-ray image enhancement algorithm based on high frequency emphasis filtering[J].Information Technology,2015(4):60-62,66. [13]王 星,白尚旺,潘理虎,等.一种矿井图像增强算法[J].工矿自动化,2017,43(3):48-52. WANG Xing,BAI Shang-wang,PAN Li-hu,et al.Mine image enhancement algorithm[J].Industrial and mining automation,2017,43(3):48-52. [14]张谢华,张 申,方 帅,等.煤矿智能视频监控中雾尘图像的清晰化研究[J].煤炭学报,2014,39(1):198-204. ZHANG Xie-hua,ZHANG Shen,FANG Shuai,et al.Research on the clearing of fog dust image in coal mine intelligent video surveillance[J].Journal of China Coal Society,2014,39(1):198-204. [15]冯 惠,吐尔洪江·阿布都克力木.基于二进小波与融合方法的医学图像增强研究[J].计算机应用与软件,2017,34(4):233-237. FENG Hui,Tul Hongjiang·Abdul Kelimu.Medical image enhancement based on binary wavelet and fusion method[J].Computer Applications & Software,2017,34(4):233-237. [16]周 飞,贾振红,杨 杰,等.基于剪切波域改进Gamma校正的医学图像增强算法[J].光电子·激光,2017,28(5):566-572. ZHOU Fei,JIA Zhen-hong,YANG Jie,et al.Medical image enhancement algorithm based on shear wave domain improved gamma correction[J].Photoelectron Laser,2017,28(5):566-572. [17]李纪成,谢 凯,阮宁君,等.基于曲波变换的医学图像增强算法[J].计算机工程与设计,2017,38(1):187-191. LI Ji-cheng,XIE Kai,RUAN Ning-jun,et al.Medical image enhancement algorithm based on curvelet transform[J].Computer Engineering and Design,2017,38(1):187-191. [18]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences,1998,454(1971):903-995. [19]Huang N E,Shen Z,Long S R.A new view of nonlinear water waves:the Hilbert spectrum[J].Annual Review of Fluid Mechanics,1999,31(1):417-457. [20]Wu Z,Huang N E,Long S R,et al.On the trend,detrending,and variability of nonlinear and nonstationary time series[J].Proceedings of the National Academy of Sciences,2007,104(38):14889-14894. [21]Zang Y,Gao Y,Wang L,et al.The removal of wall components in doppler ultrasound signals by using the empirical mode decomposition algorithm[J].IEEE Transactions on Biomedical Engineering,2007,54(9):1631-1642. [22]昝会萍,张引科,谷同凯.处理双曝光数字全息的一种新方法[J].西安科技大学学报,2017,37(5):719-723. ZAN Hui-ping,ZHANG Yin-ke,GU Tong-kai.A new method for dealing with double exposure digital holography[J].Journal of Xi'an University of Science and Technology,2017,37(5):719-723. [23]张 烨,刘晓佩.一种改进的压缩感知图像融合方法[J].西安科技大学学报,2018,38(4):690-696. ZHANG Ye,LIU Xiao-pei.An improved compressed sensing image fusion method[J].Journal of Xi'an University of Science and Technology,2018,38(4):690-696.

备注/Memo

备注/Memo:
收稿日期:2020-02-23 责任编辑:高 佳
基金项目:陕西省科技计划工业科技攻关(2015GY023,2017GY-073); 西安市碑林区应用技术研发(GX1811)
通信作者:赵 谦(1977-),男,陕西西安人,博士,副教授,E-mail:52156950@qq.com
更新日期/Last Update: 2020-05-15