Volume 49 Issue 4
Apr.  2023
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KONG Erwei, ZHANG Yabang, LI Jiayue, et al. An enhancement method for low light images in coal mines[J]. Journal of Mine Automation，2023，49(4)：62-69, 85. DOI: 10.13272/j.issn.1671-251x.2022110054
Citation: KONG Erwei, ZHANG Yabang, LI Jiayue, et al. An enhancement method for low light images in coal mines[J]. Journal of Mine Automation，2023，49(4)：62-69, 85. DOI: 10.13272/j.issn.1671-251x.2022110054
shu

An enhancement method for low light images in coal mines

  • 1.

    Pingdingshan Tian'an Coal Mining Co., Ltd., Pingdingshan 467000, China

  • 2.

    School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China

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  • Received Date: November 13, 2022
  • Revised Date: April 19, 2023
  • Available Online: April 26, 2023
    Graphical Abstract
    • Abstract
  • Underground lighting in coal mines is limited. There is a large amount of dust and mist, resulting in low contrast, uneven lighting, weak detail information, and a large amount of noise in the collected images. The image enhancement methods based on traditional models have poor robustness, often causing excessive image enhancement and color distortion. Most image enhancement methods based on deep learning do not consider the noise amplification caused by enhancement. In order to solve the above problems, an enhancement method for low light images in coal mines is proposed. The image enhancement network is constructed by using convolutional neural networks. The network includes feature extraction modules, enhancement modules, and fusion modules. The feature extraction module convolves the input image to varying degrees, extracts multi-level image features, and obtains multiple feature layers. The enhancement module enhances the extracted feature layers through sub-networks to enhance different levels of detail features. The fusion module fuses the enhanced feature layers and outputs enhanced images. Then, through the constraints of the structure loss function, content loss function and area loss function, the image quality is improved. The image color distortion and noise amplification are effectively suppressed to obtain the final enhanced image. The experimental results show that this method can effectively improve the brightness and contrast of low light images in coal mines. The method has strong noise suppression capability, enabling the image to better restore the original details while avoiding overexposure or color distortion.
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    • References (21)
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