面向煤矿井下低光照图像的增强方法

孔二伟; 张亚邦; 李佳悦; 王满利

doi:10.13272/j.issn.1671-251x.2022110054

面向煤矿井下低光照图像的增强方法

doi: 10.13272/j.issn.1671-251x.2022110054

1.
平顶山天安煤业股份有限公司, 河南平顶山　467000
2.
河南理工大学物理与电子信息学院, 河南焦作　454000

基金项目: 国家自然科学基金项目（52074305）；河南理工大学博士基金项目（B2021-64)。

详细信息

作者简介:
孔二伟（1982—），男，河南平顶山人，高级工程师，硕士，现主要从事电气工程方面的工作，E-mail：kongerwei1982@163.com

通讯作者:
李佳悦（1999—），女，山西运城人，硕士研究生，研究方向为图像处理、深度学习，E-mail：lijiayue0827@163.com

中图分类号: TD67
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- 被引次数: 0
出版历程
- 收稿日期: 2022-11-14
- 修回日期: 2023-04-20
- 网络出版日期: 2023-04-27

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

摘要

摘要: 煤矿井下照明有限，并且具有大量粉尘、雾气，使得采集到的图像对比度低、光照不均、细节信息弱，并含有大量噪声。基于传统模型的图像增强方法鲁棒性较差，常会引起图像过度增强和色彩失真；基于深度学习的图像增强方法大多没有考虑增强引起的噪声放大。针对上述问题，提出了一种面向煤矿井下低光照图像的增强方法。采用卷积神经网络构建图像增强网络，该网络包括特征提取模块、增强模块和融合模块。特征提取模块对输入图像进行不同程度的卷积，提取多层次的图像特征，得到多个特征层；增强模块对提取到的特征层通过子网络进行增强，强化不同程度的细节特征；融合模块将增强后的特征层进行融合，输出增强图像。之后通过结构损失函数、内容损失函数和区域损失函数的约束，提高图像质量并有效抑制图像颜色失真与噪声放大，得到最终的增强图像。实验结果表明，该方法能够有效提升煤矿井下低光照图像的亮度和对比度，并且具有较强的噪声抑制能力，使图像能更好地恢复原有的细节信息，同时避免出现过曝光或颜色失真。
- 煤矿低光照图像 /
- 图像增强 /
- 图像去噪 /
- 深度学习 /
- 损失函数
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.
- low light images of coal mines /
- image enhancement /
- image denoising /
- deep learning /
- loss function

HTML全文

图 1 面向煤矿井下低光照图像的增强网络整体结构

Figure 1. Overall structure of enhancement network for underground coal mine low-light image

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图 2 FEM结构

Figure 2. Structure of feature extraction module

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图 3 EM结构

Figure 3. Structure of enhancement module

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图 4 EM子网络结构

Figure 4. Sub-network structure of enhancement module

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图 5 FM结构

Figure 5. Structure of fusion module

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图 6 本文方法增强效果

Figure 6. Enhancement effect of the proposed method

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图 7 不同方法下图像增强效果对比

Figure 7. Comparison of image enhancement effect under different methods

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图 8 不同方法下图像客观评价结果对比

Figure 8. Comparison of image objective evaluation results under different methods

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图 9 不同特征层数下图像增强效果对比

Figure 9. Comparison of image enhancement effect under different feature layers

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图 10 EM添加Concat层前后增强效果对比

Figure 10. Comparison of enhancement effect before and after enhancement module adding Concat layer

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图 11 添加内容损失函数前后增强效果对比

Figure 11. Comparison of enhancement effect before and after adding context loss function

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表 1 本文方法下图像增强客观评价结果

Table 1. Objective evaluation results of image enhancement of the proposed method

图像编号 PSNR SSIM
原始图像增强图像原始图像增强图像

T112 13.54 21.89 0.43 0.78
T118 14.55 18.65 0.13 0.74
T120 12.19 21.21 0.30 0.73

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表 2 EM添加Concat层前后NIQE值对比

Table 2. Comparison of NIQE value before and after enhancement module adding Concat layer

图像编号 NIQE
无Concat层添加Concat层

T801 2.41 2.35
T822 3.78 3.59
T828 4.49 4.24
T841 2.83 2.58
平均值 3.38 3.19

下载: 导出CSV

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