CAO Huchen, YAO Shanhua, WANG Zhonggen. Defogging algorithm of underground coal mine dust and fog image based on boundary constraint[J]. Journal of Mine Automation,2022,48(6):139-146. DOI: 10.13272/j.issn.1671-251x.2022010010
Citation: CAO Huchen, YAO Shanhua, WANG Zhonggen. Defogging algorithm of underground coal mine dust and fog image based on boundary constraint[J]. Journal of Mine Automation,2022,48(6):139-146. DOI: 10.13272/j.issn.1671-251x.2022010010

Defogging algorithm of underground coal mine dust and fog image based on boundary constraint

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  • Received Date: January 05, 2022
  • Revised Date: May 29, 2022
  • Available Online: May 04, 2022
  • The existing defogging algorithms of underground coal mine images mainly include defogging algorithm based on image enhancement, defogging algorithm based on CNN and defogging algorithm based on physical model. The former two have poor defogging effect and are prone to over-exposure. The physical model-based defogging algorithm processes the dust and fog according to the atmospheric scattering model. However, the dark channel-based atmospheric light value estimation method is applied to the underground coal mine environment, and the selected atmospheric light value will be small. The problems of image overexposure, incapability of inhibiting point light source irradiation are easily caused. In order to solve the above problems, the image defogging algorithm based on dark primary color prior (He algorithm) and the defogging algorithm based on boundary constraint and context regularization (Meng algorithm) are fused. The defogging algorithm of underground coal mine dust and fog image based on boundary constraint is proposed. The method comprises the following steps. Firstly, Gamma correction is performed on the input image. The color channel opening operation processing is performed on the corrected image to obtain the low-resolution pixel block. The maximum brightness value is selected from the low-resolution pixel block as the underground atmospheric light value of the coal mine. Secondly, the Gamma-corrected image is processed by He algorithm and Meng algorithm respectively. The boundary constraint map obtained by Meng algorithm is filtered to obtain a clearer boundary constraint map. And the rough transmittance difference of Meng algorithm and He algorithm is compared and then fused. Finally, the contextual regularization is performed on fused rough transmittance to obtain the refined transmittance. The obtained atmospheric light value and the refined transmittance are used to obtain the defogged image through the atmospheric scattering model. The simulation results show that the proposed defogging algorithm of underground coal mine dust and fog image based on boundary constraint has no problems such as overexposure. The defogging effect is better, the defogged image is brighter and the color is closer to the original image. The peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM) are used to objectively evaluate the defogging effect of the proposed algorithm. The results show that the proposed algorithm has an average improvement of 61.52%, 36.51% and 24.57% in PSNR, SSIM and FSIM compared with the He algorithm. Compared with the algorithm in literature [9], the proposed algorithm has increased by 15.51%, 19.27% and −0.30% on average. Compared with Meng algorithm, the proposed algorithm has increased by 18.93%, 7.19% and 1.21% on average. Compared with the algorithm in literature [11], the proposed algorithm has increased by 18.29%, 10.54% and 1.19% on average. It shows that the proposed algorithm has better defogging effect, brighter image and more details in the underground environment of coal mine.
  • [1]
    LIU Zhigang,CAO Anye,GUO Xiaosheng,et al. Deep-hole water injection technology of strong impact tendency coal seam—a case study in Tangkou Coal Mine[J]. Arabian Journal of Geosciences,2018,11(2):1-9.
    [2]
    肖军良. 辛置煤矿2-208工作面喷雾降尘技术研究与应用[J]. 煤矿现代化,2021,30(6):46-48. DOI: 10.3969/j.issn.1009-0797.2021.06.014

    XIAO Junliang. Research and application of spray dust suppression technology in 2-208 working face of Xinzhi Coal Mine[J]. Coal Mine Modernization,2021,30(6):46-48. DOI: 10.3969/j.issn.1009-0797.2021.06.014
    [3]
    王道累,张天宇. 图像去雾算法的综述及分析[J]. 图学学报,2020,41(6):861-870.

    WANG Daolei,ZHANG Tianyu. Review and analysis of image defogging algorithm[J]. Journal of Graphics,2020,41(6):861-870.
    [4]
    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 Retinex fusion algorithm in HSV space[J]. Journal of China Coal Society, 2020, 45(S1): 532-540.
    [5]
    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):37-41,61.

    GONG Yun,YANG Pangbin,JIE Xinyu. Underground image matching algorithm combining homomorphic filtering and histogram equalization[J]. Industry and Mine Automation,2021,47(10):37-41,61.
    [6]
    智宁,毛善君,李梅,等. 基于深度融合网络的煤矿图像尘雾清晰化算法[J]. 煤炭学报,2019,44(2):655-666.

    ZHI Ning,MAO Shanjun,LI Mei,et al. Coal mine image dust and fog clearing algorithm based on deep fusion network[J]. Journal of China Coal Society,2019,44(2):655-666.
    [7]
    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
    [8]
    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.
    [9]
    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
    [10]
    MENG Gaofeng, WANG Ying, DUAN Jiangyong, et al. Efficient image dehazing with boundary constraint and contextual regularization[C]//2013 IEEE International Conference on Computer Vision, Sydney, 2013: 617-624.
    [11]
    杨红,崔艳. 基于开运算暗通道和优化边界约束的图像去雾算法[J]. 光子学报,2018,47(6):244-250.

    YANG Hong,CUI Yan. Image defogging algorithm based on opening dark channel and improved boundary constraint[J]. Acta Photonica Sinica,2018,47(6):244-250.
    [12]
    NARASIMHAN S G, NAYAR S K. Chromatic framework for vision in bad weather[C]//IEEE Computer Society Conference on Computer Vision & Pattern Recognition, Hilton Head Island, 2000: 598-605
    [13]
    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
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