Volume 48 Issue 5
May  2022
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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

  • 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

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  • Received Date: November 19, 2021
  • Revised Date: April 27, 2022
  • Available Online: March 14, 2022
    Graphical Abstract
    • 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)
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