A algorithm for low light image enhancement in coal mine underground based on illumination constraints
To address the issues of low visibility, insufficient exposure, and blurred details in images collected in coal mine environments, this research introduces a low light image enhancement algorithm, which is grounded in illumination constraints. The algorithm structure consists of three main modules:...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Safety in Coal Mines
2025-03-01
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| Series: | Meikuang Anquan |
| Subjects: | |
| Online Access: | https://www.mkaqzz.com/cn/article/doi/10.13347/j.cnki.mkaq.20240748 |
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| Summary: | To address the issues of low visibility, insufficient exposure, and blurred details in images collected in coal mine environments, this research introduces a low light image enhancement algorithm, which is grounded in illumination constraints. The algorithm structure consists of three main modules: the illumination constraint module (ICM), the illumination decomposition module (IDM), and the illumination enhancement module (IEM). The ICM captures the overall light distribution of the image, creates a grayscale attention map to minimize illumination interference, and the IDM decomposes the image into illumination and reflection components. The IEM uses a U-Net network structure to enhance the illumination component. The enhanced illumination component is then combined with the grayscale attention map and reflection component to produce the enhanced image. Both ICM and IDM incorporate an efficient channel attention module (ECA), which regulates light distribution and enhances the feature capture capability for illumination and reflection components. Experiments were conducted in four different scenarios, comparing this algorithm against TBEFN, RUAS, MBLLEN, KinD, and Retinex-Net algorithms. Results indicate that this algorithm surpasses others in visual information fidelity (VIF), structural similarity index metric (SSIM), and peak signal to noise ratio (PSNR), achieving averages of 0.58, 0.61, and 16.58 respectively. Compared to the original model, it showed improvements of approximately 23.40%, 16.07%, and 20.45% in these metrics, demonstrating optimal image enhancement effectiveness. |
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| ISSN: | 1003-496X |