Volume 48 Issue 5
May  2022
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Article Navigation >  Journal of Mine Automation  > 2022  >  48(5): 46-51, 84
WANG Yuanbin, WEI Sixiong, DUAN Yu, et al. Defogging algorithm of underground coal mine image based on adaptive dual-channel prior[J]. Journal of Mine Automation，2022，48(5)：46-51, 84.  doi: 10.13272/j.issn.1671-251x.2021110053
Citation: WANG Yuanbin, WEI Sixiong, DUAN Yu, et al. Defogging algorithm of underground coal mine image based on adaptive dual-channel prior[J]. Journal of Mine Automation，2022，48(5)：46-51, 84.  doi: 10.13272/j.issn.1671-251x.2021110053
shu

Defogging algorithm of underground coal mine image based on adaptive dual-channel prior

doi: 10.13272/j.issn.1671-251x.2021110053
  • 1.

    College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

  • 2.

    Shaanxi Key Laboratory of Intelligent Monitoring of Mine Electromechanical Equipment, Xi'an 710054, China

  • Received Date: 2021-11-20
  • Rev Recd Date: 2022-04-28
    • Available Online: 2022-03-15
    Abstract
  • When dark channel prior algorithm is used to deal with underground coal mine images, there are problems of image distortion, lack of details and dark light. In order to solve the above problems, a defogging algorithm of underground coal mine image based on adaptive dual-channel prior is proposed. Firstly, according to the physical model of atmospheric scattering and the special environment of underground coal mine, the dust and fog image degradation model in underground coal mine is established. Secondly, a dual-channel prior model is established by fusing the dark channel and the bright channel to optimize the transmittance. An adaptive weight coefficient is added to improve the precision of the transmittance image. And the gradient guided filtering is adopted to replace the traditional guided filtering to refine the transmittance image. Finally, combined with the mine environment, the atmospheric light value calculation method is improved. And the image is restored according to the dust and fog image degradation model. The experimental results show that the algorithm can effectively remove the fog phenomenon in the image, avoid the halo blur and over-enhancement phenomenon. Compared with dark channel prior algorithm, Retinex algorithm and Tarel algorithm, this algorithm greatly improves the image information entropy and average gradient. The algorithm enriches the detailed information of the restored image and shortens the running time.

     

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    • References(17)
  • [1]
    范伟强,刘毅. 基于自适应小波变换的煤矿降质图像模糊增强算法[J]. 煤炭学报,2020,45(12):4248-4260.

    FAN Weiqiang,LIU Yi. Fuzzy enhancement algorithm of coal mine degradation image based on adaptive wavelet transform[J]. Journal of China Coal Society,2020,45(12):4248-4260.
    [2]
    郭瑞,党建武,沈瑜,等. 改进的单尺度Retinex图像去雾算法[J]. 兰州交通大学学报,2018,37(6):69-75. doi: 10.3969/j.issn.1001-4373.2018.06.011

    GUO Rui,DANG Jianwu,SHEN Yu,et al. Fog removal algorithm of improved single scale Retinex image[J]. Journal of Lanzhou Jiaotong University,2018,37(6):69-75. doi: 10.3969/j.issn.1001-4373.2018.06.011
    [3]
    龚云,杨庞彬,颉昕宇. 结合同态滤波与直方图均衡化的井下图像匹配算法[J]. 工矿自动化,2021,47(10):37-41.

    GONG Yun,YANG Pangbin,JIE Xinyu. Underground image matching algorithm combining homomorphic filtering and histogram equalization[J]. Industry and Mine Automation,2021,47(10):37-41.
    [4]
    刘晓阳,乔通,乔智. 基于双边滤波和Retinex算法的矿井图像增强方法[J]. 工矿自动化,2017,43(2):49-45.

    LIU Xiaoyang,QIAO Tong,QIAO Zhi. Image enhancement method of mine based on bilateral filtering and Retinex algorithm[J]. Industry and Mine Automation,2017,43(2):49-45.
    [5]
    智宁,毛善君,李梅. 基于照度调整的矿井非均匀照度视频图像增强算法[J]. 煤炭学报,2017,42(8):2190-2197.

    ZHI Ning,MAO Shanjun,LI Mei. Enhancement algorithm based on illumination adjustment for nonuniform illuminance video images in coal mine[J]. Journal of China Coal Society,2017,42(8):2190-2197.
    [6]
    HE Kaiming,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
    [7]
    王启明,李季. 煤矿井下高清图像快速去雾算法研究[J]. 小型微型计算机系统,2018,39(11):2557-2560. doi: 10.3969/j.issn.1000-1220.2018.11.038

    WANG Qiming,LI Ji. Study on fast haze removal algorithm for underground high definition image[J]. Journal of Chinese Computer Systems,2018,39(11):2557-2560. doi: 10.3969/j.issn.1000-1220.2018.11.038
    [8]
    杜明本,陈立潮,潘理虎. 基于暗原色理论和自适应双边滤波的煤矿尘雾图像增强算法[J]. 计算机应用,2015,35(5):1435-1438,1448. doi: 10.11772/j.issn.1001-9081.2015.05.1435

    DU Mingben,CHEN Lichao,PAN Lihu. Enhancement algorithm for fog and dust images in coal mine based on dark channel prior theory and bilateral adaptive filter[J]. Journal of Computer Applications,2015,35(5):1435-1438,1448. doi: 10.11772/j.issn.1001-9081.2015.05.1435
    [9]
    NARASIMHAN S G,NAYAR K. Vision and the atmosphere[J]. International Journal of Computer Vision,2002,48(3):233-254. doi: 10.1023/A:1016328200723
    [10]
    XU Yueshu, GUO Xiaoqiang, WANG Haiying, et al. Single image haze removal using light and dark channel prior[C]//2016 IEEE/CIC International Conference on Communications in China （ICCC）, Piscataway, 2016: 1-6.
    [11]
    蒯峰阳,张丹. 基于亮暗通道相结合的自适应图像去雾算法[J]. 计算技术与自动化,2021,40(2):118-124.

    KUAI Fengyang,ZHANG Dan. Adaptive single image haze removal using integrated dark and bright channel prior[J]. Computing Technology and Automation,2021,40(2):118-124.
    [12]
    HE Kaiming,SUN Jian,TANG Xiao'ou. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(6):1397-1409. doi: 10.1109/TPAMI.2012.213
    [13]
    YU Teng,SONG Kang,MIAO Pu,et al. Nighttime single image dehazing via pixel-wise alpha blending[J]. IEEE Access,2019,7:114619-114630. doi: 10.1109/ACCESS.2019.2936049
    [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 Xiehua,ZHANG Shen,FANG Shuai,et al. Clearing research on fog and dust images in coalmine intelligent video surveillance[J]. Journal of China Coal Society,2014,39(1):198-204.
    [15]
    KOU Fei,CHEN Weihai,WEN Changyun,et al. Gradient domain guided image filtering[J]. IEEE Transactions on Image Processing,2015,24(11):4528-4539. doi: 10.1109/TIP.2015.2468183
    [16]
    刘晓文,仲亚丽,袁莎莎,等. 基于暗原色先验的煤矿井下退化图像复原算法[J]. 煤炭科学技术,2012,40(6):77-80.

    LIU Xiaowen,ZHONG Yali,YUAN Shasha,et al. Restoration algorithms of degradation image in underground mine based on dark channel prior[J]. Coal Science and Technology,2012,40(6):77-80.
    [17]
    张英俊,雷耀花,潘理虎. 基于暗原色先验的煤矿井下图像增强技术[J]. 工矿自动化,2015,41(3):80-83.

    ZHANG Yingjun,LEI Yaohua,PAN Lihu. Enhancement technique of underground image based on dark channel prior[J]. Industry and Mine Automation,2015,41(3):80-83.
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