Volume 50 Issue 6
Jun.  2024
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Article Navigation >  Journal of Mine Automation  > 2024  >  50(6): 79-88, 111
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
shu

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

doi: 10.13272/j.issn.1671-251x.2023080126
  • 1.

    College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China

  • 2.

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

  • Received Date: 2023-08-31
  • Rev Recd Date: 2024-06-22
    • Available Online: 2024-06-27
    Abstract
  • 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.

     

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