YUAN Mingdao, TAN Cai, LI Yang, XU Yunqian, ZHANG Xuhui, YANG Jingxue. A pipeline robot detection image enhancement method based on image fusion and improved threshold[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(4): 178-185. DOI: 10.3969/j.issn.1001-1986.2019.04.027
Citation: YUAN Mingdao, TAN Cai, LI Yang, XU Yunqian, ZHANG Xuhui, YANG Jingxue. A pipeline robot detection image enhancement method based on image fusion and improved threshold[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(4): 178-185. DOI: 10.3969/j.issn.1001-1986.2019.04.027

A pipeline robot detection image enhancement method based on image fusion and improved threshold

Funds: 

Water Resource Science and Technology Innovation Program of Shenzhen(20170103)

More Information
  • Received Date: August 24, 2018
  • Published Date: August 24, 2019
  • Pipeline robot detection technology can quickly, accurately and intuitively identify the structure and hidden functional troubles of pipeline. However, due to the restriction of the pipeline environment, the detected images have problems such as uneven illumination, low contrast and blurred details. Therefore, an enhancement technique for detected image of pipeline robot is proposed. First, the contrast limited adaptive histogram equalization(CLAHE) and homomorphic filtrate(HF) are applied to deal with the problem of uneven illumination and low contrast, and the result images of the two methods are fused. Secondly, the fusion images are transformed by the Nonsubsampled Contourlet Transform(NSCT), and the improved Bayes-Shrink threshold is used to remove the noise of the high frequency coefficient. Finally, the nonlinear mapping function is used to enhance the details, and the NSCT inverse transform is used to get the final enhanced image. In order to verify the effectiveness and superiority of the method for pipeline robot detection image, 5 typical pipeline robot detection images were selected and enhanced by this method, and compared with 4 common image enhancement technologies. The results show that image enhancement method for pipeline robot detection image by using image fusion and improved threshold can effectively improve the overall and local contrast image, and effectively enhance the image details. It can solve the main problems in pipeline robot detected image effectively.
  • [1]
    徐云乾. CCTV和三维激光扫描技术在水利工程输排水管道隐患探测中的应用[J]. 中国农村水利水电,2014(3):68-70.

    XU Yunqian. The application of CCTV and 3D laser scanning technology in the detection of hidden dangers of drainage pipeline in water conservancy engineering[J]. China Rural Water and Hydropower,2014(3):68-70.
    [2]
    GUO X,LI Y,LING H. LIME:Low-light image enhancement via illumination map estimation[J]. IEEE Transactions on Image Processing a Publication of the IEEE Signal Processing Society,2017,26(2):982-993.
    [3]
    LIU H,LU H,ZHANG Y. Image enhancement for outdoor long-range surveillance using IQ-learning multiscale Reti-nex[J]. Iet Image Processing,2017,11(9):786-795.
    [4]
    KAUR A,SINGH C. Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization[J]. Applied Soft Computing,2017,51:180-191.
    [5]
    李阳,常霞,纪峰. 图像增强方法研究新进展[J]. 传感器与微系统,2015,34(12):9-12.

    LI Yang,CHANG Xia,JI Feng. New progress of research on image enhancement method[J].Transducer and Microsystem Technologies,2015,34(12):9-12.
    [6]
    GHARBI M,CHEN J,BARRON J T,et al. Deep bilateral learning for real-time image enhancement[J]. ACM Transac-tions on Graphics,2017,36(4):118.
    [7]
    WANG Y,PAN Z. Image contrast enhancement using adja-cent-blocks-based modification for local histogram equalization[J]. Infrared Physics & Technology,2017,86:59-65.
    [8]
    VOICU L I. Practical considerations on color image enhancement using homomorphic filtering[J]. Journal of Electronic Imaging,1997,6(1):108-113.
    [9]
    YANG L,XIA C,CHANG J. Image edge detection based on gaussian mixture model in nonsubsampled contourlet do-main[J]. Journal of Electrical and Computer Engineering,2016(10):1-10.
    [10]
    HEPSIBAH K,HEAVEN M S,SARAVANAN M. Image enhancement based on nonsubsampled contourlet transform using matrix factorization techniques[J]. International Journal of Computer Applications,2015,123(6):35-38.
    [11]
    闫利,向天烛. NSCT域内结合边缘特征和自适应PCNN的红外与可见光图像融合[J]. 电子学报,2016,44(4):761-766.

    YAN Li,XIANG Tianzhu. Fusion of infrared and visible images based on edge feature and adaptive PCNN in NSCT domain[J]. Acta Electronica Sinica,2016,44(4):761-766.
    [12]
    周飞,贾振红,杨杰,等. 基于NSCT和改进模糊的遥感图像增强方法[J]. 计算机工程与应用,2017,53(15):206-209.

    ZHOU Fei,JIA Zhenhong,YANG Jie,et al. Enhancement method of remote sensing image based on NSCT and improved fuzzy contrast[J]. Computer Engineering and Applications,2017,53(15):206-209.
    [13]
    吴一全,史骏鹏. 基于多尺度Retinex的非下采样Contourlet域图像增强[J]. 光学学报,2015,35(3):79-88.

    WU Yiquan,SHI Junpeng. Image enhancement in non-subsampled Contourlet transform domain based on multi-scale Retinex[J]. Acta Optica Sinica,2015,35(3):79-88.
    [14]
    王峰,程咏梅. 基于MSSTO与NSCT变换的可见光与红外图像增强融合[J]. 控制与决策,2017,32(2):269-274.

    WANG Feng,CHENG Yongmei. Visible and infrared image enhanced fusion based on MSSTO and NSCT transform[J]. Control and Decision,2017,32(2):269-274.
    [15]
    XIAO L,LI C,WU Z,et al. An enhancement method for X-ray image via fuzzy noise removal and homomorphic filtering[J]. Neurocomputing,2016,195:56-64.
    [16]
    肖进胜,单姗姗,段鹏飞,等. 基于不同色彩空间融合的快速图像增强算法[J]. 自动化学报,2014,40(4):697-705.

    XIAO Jinsheng,SHAN Shanshan,DUAN Pengfei,et al. A fast image enhancement algorithm based on fusion of different color spaces[J]. 2014,40(4):697-705.
    [17]
    HE H,LEE W J,LUO D S,et al. Insulator infrared image denoising method based on wavelet generic gaussian distribution and map estimation[J]. IEEE Transactions on Industry Applications,2017,53(4):3279-3284.
    [18]
    付晓薇,丁明跃,周成平,等. 基于量子概率统计的医学图像增强算法研究[J]. 电子学报,2010,38(7):1590-1596.

    FU Xiaowei,DING Mingyue,ZHOU Chengping,et al. Research on image enhancement algorithms of medical images based on quantum probability statistics[J]. Acta Electronica Sinica,2010,38(7):1590-1596.
  • Related Articles

    [1]MENG Zhaoping, ZHANG Kun, SHEN Zhen. Difference analysis of methane diffusion properties between tectonic coal and primary coal[J]. COAL GEOLOGY & EXPLORATION, 2022, 50(3): 102-109. DOI: 10.12363/issn.1001-1986.21.12.0799
    [2]ZHOU Xihua, HAN Mingxu, BAI Gang, LAN Anchang, FU Zhihao. Experimental study on the influence of CO2 injection pressure on gas diffusion coefficient[J]. COAL GEOLOGY & EXPLORATION, 2021, 49(1): 81-86,99. DOI: 10.3969/j.issn.1001-1986.2021.01.008
    [3]YANG Zhaozhong, HAN Jinxuan, ZHANG Jian, HE Rui, LU Yanjun, LI Xiaogang. Molecular simulation of the influence of foam fracturing fluid additives on coalbed methane diffusion[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(5): 94-103. DOI: 10.3969/j.issn.1001-1986.2019.05.013
    [4]MENG Zhaoping, ZHANG Guiyuan, LI Guoqing, LIU Jinrong. Analysis of diffusion properties of methane in low rank coal[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(2): 84-89. DOI: 10.3969/j.issn.1001-1986.2019.02.014
    [5]LIN Chen, JIA Tianrang, ZHOU Shiwei, ZHANG Yugui. Diffusion characteristics of methane adsorption process in granular coal[J]. COAL GEOLOGY & EXPLORATION, 2018, 46(4): 44-49. DOI: 10.3969/j.issn.1001-1986.2018.04.007
    [6]LIN Yabing, MA Dongmin, LIU Yuhui, MA Wei, JIA Xuemei. Experiment of the influence of temperature on coalbed methane adsorption[J]. COAL GEOLOGY & EXPLORATION, 2012, 40(6): 24-28. DOI: 10.3969/j.issn.1001-1986.2012.06.006
    [7]HE Jun, HAO Guo-wen. Relationship between hydraulic conductivity and diffusion coefficient of clay liner[J]. COAL GEOLOGY & EXPLORATION, 2007, 35(6): 40-43.
    [8]LI Yu-hui, CUI Yong-jun, ZHONG Ling-wen, JIANG Wen-ping. Study on dynamic diffusion characteristics of methane in coal matrix[J]. COAL GEOLOGY & EXPLORATION, 2005, 33(6): 31-34.
    [9]NIE Baisheng, ZHANG Li, MA Wenfang. DIFFUSION MICRO-MECHANISM OF COAL BED METHANE IN COAL PROES[J]. COAL GEOLOGY & EXPLORATION, 2000, 28(6): 20-22.
    [10]WEI Zhong-tao, LIU Huan-jie, MENG Jian. NUMERICAL SIMULATION ON COALBED METHANE DIFFUSION IN GEOHISTORY[J]. COAL GEOLOGY & EXPLORATION, 1998, 26(5): 19-24.
  • Cited by

    Periodical cited type(8)

    1. 蓝龙飞. 在冻融条件下某露天矿山边坡变形特征研究. 矿产勘查. 2024(S1): 43-46 .
    2. 梁博,杨更社,冯伟,潘振兴,孙杰龙,刘慧,陈奇. 冻融诱发平面滑移型岩质边坡失稳模型试验研究. 西安科技大学学报. 2024(06): 1118-1126 .
    3. 张庆武,阴子晔,刘树弟. 岩土冻结的主要影响因素分析及应对措施. 煤炭与化工. 2023(10): 7-10+18 .
    4. 王云. 分析高陡岩土边坡绿色生态环境修复技术. 世界有色金属. 2022(09): 211-213 .
    5. 陈军浩,庄言,陈笔尖,赵振伟,王启云. 滨海软土冻结温度场发展规律. 煤田地质与勘探. 2020(04): 174-182 . 本站查看
    6. 谭捍华,李斌,李家欣,袁维,李宗鸿. 冻融循环作用下白云岩边坡的稳定性分析. 科学技术与工程. 2020(33): 13825-13832 .
    7. 李国锋,李宁,刘乃飞,朱才辉. 多年冻岩土区露天矿边坡局部稳定性探究. 西安理工大学学报. 2019(01): 53-61 .
    8. 李伟. 分析高陡岩土边坡绿色生态环境修复技术. 居舍. 2018(32): 46 .

    Other cited types(9)

Catalog

    Article Metrics

    Article views (144) PDF downloads (12) Cited by(17)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return