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Jan.  2024
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ZHANG Yabang, LI Jiayue, WANG Manli. An Algorithm for Low-Light Image Enhancement in Coal Mines Based on HSV Space[J]. Infrared Technology , 2024, 46(1): 74-83.
Citation: ZHANG Yabang, LI Jiayue, WANG Manli. An Algorithm for Low-Light Image Enhancement in Coal Mines Based on HSV Space[J]. Infrared Technology , 2024, 46(1): 74-83.
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An Algorithm for Low-Light Image Enhancement in Coal Mines Based on HSV Space

  • 1.

    Pingdingshan Tian'an Coal Industry Co., Ltd. Sixth Mine, Pingdingshan 467000, China

  • 2.

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

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  • Received Date: May 11, 2023
  • Revised Date: August 06, 2023
    Graphical Abstract
    • Abstract
  • A low light image enhancement method based on HSV space is proposed to address the issues of low contrast, uneven lighting, and weak detail information in images collected underground in coal mines. This method is based on the Hue-Saturation-Luminance(HSV) color space of the image, and enhances the contrast of the main structure and edge details of the brightness channel V channel in low light images. This can better suppress image detail loss and reproduce the contour and texture details in the original image. Firstly, the input low light image of the coal mine underground is converted into HSV space, and the main structure and contour edges of the extracted V-channel image are extracted and preserved using relative total variation filtering (RTV) and improved side window filtering (SWF). After nonlinear grayscale stretching, principal component analysis fusion technology (PCA) is used to reconstruct the V-channel image, which fuses the main structure and fine structure of the V-channel image. Finally, the image is synthesized, Complete image enhancement. Through experimental verification, the proposed low light image enhancement method for coal mine underground based on HSV space performs well in color and edge blur processing. Quantitative and qualitative experiments were conducted on images in environments such as coal mine underground working faces. The results show that compared with the six algorithms, the proposed method performs better in improving image contrast, enhancing image naturalness, and restoring image details.
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    • References (25)
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