Volume 50 Issue 6
Jun.  2024
Turn off MathJax
Article Contents
MU Qi, GE Xiangfu, WANG Xinyue, et al. A coal mine underground image enhancement method based on multi-scale gradient domain guided image filtering[J]. Journal of Mine Automation,2024,50(6):79-88, 111.  doi: 10.13272/j.issn.1671-251x.2023080126
Citation: MU Qi, GE Xiangfu, WANG Xinyue, et al. A coal mine underground image enhancement method based on multi-scale gradient domain guided image filtering[J]. Journal of Mine Automation,2024,50(6):79-88, 111.  doi: 10.13272/j.issn.1671-251x.2023080126

A coal mine underground image enhancement method based on multi-scale gradient domain guided image filtering

doi: 10.13272/j.issn.1671-251x.2023080126
  • Received Date: 2023-08-31
  • Rev Recd Date: 2024-06-22
  • Available Online: 2024-06-27
  • There are serious issues with uneven lighting and noise interference in coal mine underground images. The existing Retinex based methods are directly applied to enhance coal mine underground images, which are prone to problems such as halo artifacts, blurred edges, over enhancement, and noise amplification. In order to solve the above problems, a coal mine underground image enhancement method based on multi-scale gradient domain guided image filtering is proposed. Firstly, the multi-scale idea is introduced into gradient domain guided image filtering to achieve accurate estimation of non-uniform lighting, effectively solving the problems of halo artifacts and edge blurring in enhanced images. Secondly, the Retinex model is used to separate the lighting component and reflection component. For the lighting component, the lighting information is corrected pixel by pixel through an adaptive gamma correction function, which enhances the dark areas of the image while suppressing the over enhancement of the bright areas. The image contrast is adjusted using a contrast limited adaptive histogram equalization method. For the reflection component, gradient domain guided image filtering is combined with multi-scale detail enhancement to accurately remove noise and improve texture details, avoiding the problem of noise amplification during image enhancement. Finally, the processed lighting and reflection components are fused, and the image gain coefficient is calculated. The linear color restoration method is used to enhance the original RGB image pixel by pixel, improving the processing efficiency of the method. The experimental results show that, from a subjective and objective perspective, compared with existing methods, the images processed by the proposed method have achieved better enhancement effects in color preservation, contrast, noise suppression, detail preservation, and other aspects, while also having higher processing efficiency.

     

  • loading
  • [1]
    王国法,刘峰,庞义辉,等. 煤矿智能化——煤炭工业高质量发展的核心技术支撑[J]. 煤炭学报,2019,44(2):349-357.

    WANG Guofa,LIU Feng,PANG Yihui,et al. Coal mine intellectualization:the core technology of high quality development[J]. Journal of China Coal Society,2019,44(2):349-357.
    [2]
    张立亚,郝博南,孟庆勇,等. 基于HSV空间改进融合Retinex算法的井下图像增强方法[J]. 煤炭学报,2020,45(增刊1):532-540.

    ZHANG Liya,HAO Bonan,MENG Qingyong,et al. Method of image enhancement in coal mine based on improved retex fusion algorithm in HSV space[J]. Journal of China Coal Society,2020,45(S1):532-540.
    [3]
    程德强,钱建生,郭星歌,等. 煤矿安全生产视频AI识别关键技术研究综述[J]. 煤炭科学技术,2023,51(2):349-365.

    CHENG Deqiang,QIAN Jiansheng,GUO Xingge,et al. Review on key technologies of AI recognition for videos in coal mine[J]. Coal Science and Technology,2023,51(2):349-365.
    [4]
    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.
    [5]
    王国法,张良,李首滨,等. 煤矿无人化智能开采系统理论与技术研发进展[J]. 煤炭学报,2023,48(1):34-53.

    WANG Guofa,ZHANG Liang,LI Shoubin,et al. Progresses in theory and technological development of unmanned smart mining system[J]. Journal of China Coal Society,2023,48(1):34-53.
    [6]
    LI Chongyi,GUO Chunle,HAN Linghao,et al. Lighting the darkness in the deep learning era[Z]. 2021. DOI: 10.48550/arXiv.2104.10729.
    [7]
    王焱,关南楠,刘海涛. 改进的多尺度Retinex井下图像增强算法[J]. 辽宁工程技术大学学报(自然科学版),2016,35(4):440-443. doi: 10.11956/j.issn.1008-0562.2016.04.020

    WANG Yan,GUAN Nannan,LIU Haitao. An improved multi-scale Retinex algorithm for mine image enhancement[J]. Journal of Liaoning Technical University(Natural Science),2016,35(4):440-443. doi: 10.11956/j.issn.1008-0562.2016.04.020
    [8]
    甘建旺. 矿井图像的去噪与增强算法研究[D]. 北京:北京石油化工学院,2021.

    GAN Jianwang. Research on denoising and enhancement algorithm of mine image[D]. Beijing:Beijing Institute of Petrochemical Technology,2021.
    [9]
    JOBSON D J,RAHMAN Z,WOODELL G A. Properties and performance of a center/surround Retinex[J]. IEEE Transactions on Image Processing,1997,6(3):451-462. doi: 10.1109/83.557356
    [10]
    JOBSON D J,RAHMAN Z. A multiscale Retinex for bridging the gap between color images and the human observation of scenes[J]. IEEE Transactions on Image Processing,1997,6(7):965-976. doi: 10.1109/83.597272
    [11]
    RAHMAN Z,JOBSON D,WOODELL G A. Retinex processing for automatic image enhancement[J]. Journal of Electronic Imaging,2004,13(1):100-110. doi: 10.1117/1.1636183
    [12]
    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 Deqiang,ZHENG Zhen,JIANG Hailong. An image enhancement algorithm for coal mine underground[J]. Industry and Mine Automation,2015,41(12):31-34.
    [13]
    智宁,毛善君,李梅. 基于双伽马函数的煤矿井下低亮度图像增强算法[J]. 辽宁工程技术大学学报(自然科学版),2018,37(1):191-197. doi: 10.11956/j.issn.1008-0562.2018.01.034

    ZHI Ning,MAO Shanjun,LI Mei. An enhancement algorithm for coal mine low illumination images based on bi-gamma function[J]. Journal of Liaoning Technical University(Natural Science),2018,37(1):191-197. doi: 10.11956/j.issn.1008-0562.2018.01.034
    [14]
    HE Kaiming,SUN Jian,TANG Xiaoou. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,35(6):1397-1409.
    [15]
    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 Shangwang,PAN Lihu,et al. A mine image enhancement algorithm[J]. Industry and Mine Automation,2017,43(3):48-52.
    [16]
    洪炎,朱丹萍,龚平顺. 基于TopHat加权引导滤波的Retinex矿井图像增强算法[J]. 工矿自动化,2022,48(8):43-49.

    HONG Yan,ZHU Danping,GONG Pingshun. Retinex mine image enhancement algorithm based on TopHat weighted guided filtering[J]. Journal of Mine Automation,2022,48(8):43-49.
    [17]
    苏波,李超,王莉. 基于多权重融合策略的Retinex矿井图像增强算法[J]. 煤炭学报,2023,48(增刊2):813-822.

    SU Bo,LI Chao,WANG Li. Mine image enhancement algorithm based on Retinex using multi-weight fusion strategy[J]. Journal of China Coal Society,2023,48(S2):813-822.
    [18]
    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
    [19]
    马龙,马腾宇,刘日升. 低光照图像增强算法综述[J]. 中国图象图形学报,2022,27(5):1392-1409. doi: 10.11834/jig.210852

    MA Long,MA Tengyu,LIU Risheng. The review of low-light image enhancement[J]. Journal of Image and Graphics,2022,27(5):1392-1409. doi: 10.11834/jig.210852
    [20]
    WEI Chen,WANG Wenjing,YANG Wenhan,et al. Deep Retinex decomposition for low-light enhancement[Z]. 2018. DOI: 10.48550/arXiv.1808.04560.
    [21]
    ZHANG Yonghua,ZHANG Jiawan,GUO Xiaojie. Kindling the darkness:a practical low-light image enhancer[C]. The 27th ACM International Conference on Multimedia,Nice,2019:1632-1640.
    [22]
    JIANG He,CAI Huangkai,YANG Jie. Learning in-place residual homogeneity for image detail enhancement[C]. IEEE International Conference on Acoustics,Speech and Signal Processing,Calgary,2018:1428-1432.
    [23]
    JIANG He,ASAD M,LIU Jingjing,et al. Single image detail enhancement via metropolis theorem[J]. Multimedia Tools and Applications,2024,83(12):36329-36353.
    [24]
    WANG Liwen,LIU Zhisong,SIU W C,et al. Lightening network for low-light image enhancement[J]. IEEE Transactions on Image Processing,2020,29:7984-7996. doi: 10.1109/TIP.2020.3008396
    [25]
    ZUIDERVELD K. Contrast limited adaptive histogram equalization[J]. Graphics Gems,1994,5:474-485.
    [26]
    KIM Y,KOH Y,LEE C,et al. Dark image enhancement based on pairwise target contrast and multi-scale detail boosting[C]. IEEE International Conference on Image Processing,Quebec City,2015:1404-1408.
    [27]
    WANG Shuhang,ZHENG Jin,HU Haimiao,et al. Naturalness preserved enhancement algorithm for non-uniform illumination images[J]. IEEE Transactions on Image Processing,2013,22(9):3538-3548. doi: 10.1109/TIP.2013.2261309
    [28]
    LI Mading,LIU Jiaying,YANG Wenhan,et al. Structure-revealing low-light image enhancement via robust Retinex model[J]. IEEE Transactions on Image Processing,2018,27(6):2828-2841. doi: 10.1109/TIP.2018.2810539
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(6)

    Article Metrics

    Article views (110) PDF downloads(16) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return