矿井井下非均匀照度图像增强算法

苗作华; 赵成诚; 朱良建; 刘代文; 陈澳光

doi:10.13272/j.issn.1671-251x.2023060032

矿井井下非均匀照度图像增强算法

doi: 10.13272/j.issn.1671-251x.2023060032

1.
武汉科技大学资源与环境工程学院，湖北武汉　430081
2.
冶金矿产资源高效利用与造块湖北省重点实验室，湖北武汉　430081

基金项目: 国家自然科学基金项目（41071242，41971237）；教育部产学合作协同育人项目（202102136008）。

详细信息

作者简介:
苗作华（1974—），男，湖北武汉人，教授，博士，硕士研究生导师，研究方向为人工智能与智慧矿山，E-mail：z837089200@wust.edu.cn

中图分类号: TD67
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出版历程
- 收稿日期: 2023-06-10
- 修回日期: 2023-11-05
- 网络出版日期: 2023-11-15

Image enhancement algorithm for non-uniform illumination in underground mines

1.
School of Resources and Environmental Engineering , Wuhan University of Science and Technology, Wuhan 430081, China
2.
Hubei Key Laboratory of Efficient Utilization and Blocking of Metallurgical Mineral Resources, Wuhan 430081, China

摘要

摘要: 矿井井下视频采集过程中由于照明系统分布不均匀、环境中存在大量粉尘和雾气，导致监控画面图像存在局部光线过曝、局部亮度不足、对比度低和边缘信息弱等问题。针对上述问题，提出了一种矿井井下非均匀照度图像增强算法。该算法基于Retinex−Net网络结构改进，具体包括非均匀光照抑制模块（NLSM）、光照分解模块（LDM）和图像增强模块（IEM）3个部分：NLSM对图像中人工光源局部非均匀光照进行抑制；LDM将图像分解为光照层和反射层；IEM对图像光照层增强，经伽马校正，最终得到增强图像。在NLSM和LDM中均采用Resnet作为网络基础架构，并顺序引入了卷积注意力机制中通道注意力模块和空间注意力模块，以增强对图像光照特征关注度和特征选择的效率。实验结果表明：① 选取MBLLEN，RUAS，zeroDCE，zeroDCE++，Retinex−Net，KinD++及非均匀照度图像增强算法对多种场景（井下运输环境场景、单光源巷道场景、多光源巷道场景、矿石场景）图像进行增强处理及定性分析，分析结果指出非均匀照度图像增强算法能够避免人工光源区域的过度增强，未在光源区域产生晕染和模糊现象，不易产生色偏，对比度适中，画面视觉效果更真实。② 选取信息熵（IE）、平均梯度（AG）、标准差（SD）、自然图像质量评价指标（NIQE）、结构相似性（SSIM）和峰值信噪比（PSNR）作为评价指标，定量比较图像增强画面质量。结果表明非均匀照度图像增强算法在多种场景下处于相对领先地位。③ 消融实验结果表明，非均匀照度图像增强算法在NIQE，SSIM，PSNR这3个评价指标上均获得了最优结果。
- 井下低光照图像 /
- 非均匀照度图像 /
- 图像增强 /
- Retinex−Net /
- 图像亮度 /
- 残差网络 /
- 注意力机制
Abstract: Due to the non-uniform distribution of lighting systems and the presence of a large amount of dust and mist in the environment during the underground video collection process, there are problems with local light overexposure, insufficient brightness, low contrast, and weak edge information in the monitoring image. In order to solve the above problems, an image enhancement algorithm for non-uniform illumination in underground mines is proposed. This algorithm is based on the improvement of Retinex-Net network structure, which includes three parts: non-uniform illumination suppression module (NLSM), illumination decomposition module (LDM), and image enhancement module (IEM). Among them, NLSM suppresses local non-uniform illumination of artificial light sources in the image. LDM decomposes the image into light and reflection layers. IEM enhances the illumination layer of the image, undergoes gamma correction, and ultimately obtains the enhanced image. Resnet is adopted as the infrastructure of the network in both NLSM and LDM. The channel attention module and spatial attention module in the convolutional attention mechanism are sequentially introduced to enhance the attention to image lighting features and the efficiency of feature selection. The experimental results show the following points. ① MBLLEN, RUAS, zeroDCE, zeroDCE++, Retinex−Net, KinD++, and non-uniform illumination image enhancement algorithms are selected to enhance and qualitatively analyze images in various scenarios (underground transportation environment, single light source roadway, multi light source roadway, ore scenario). The analysis results indicate that non-uniform illumination image enhancement algorithms can avoid excessive enhancement of artificial light source areas. There is no halo or blurring phenomenon in the light source area, and colors are not prone to color deviation. The contrast is moderate, and the visual effect of the image is more realistic. ② The information entropy (IE), average gradient (AG), standard deviation (SD), naturalness image quality evaluator (NIQE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) are selected as evaluation indicators to quantitatively compare the quality of image enhancement images. The non-uniform illumination image enhancement algorithm is also in a relatively leading position in various scenarios. ③ The ablation experimental results show the non-uniform illumination image enhancement algorithm achieves optimal results on three evaluation indicators: NIQE, SSIM, and PSNR.
- underground low light image /
- non uniform illumination images /
- image enhancement /
- Retinex-Net /
- image brightness /
- residual network /
- attention mechanism

HTML全文

图 1 Retinex−Net网络结构

Figure 1. Retinex-Net network structure

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图 2 非均匀照度图像增强算法整体网络结构

Figure 2. Overall network structure of the non-uniform illumination image enhancement algorithm

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图 3 Layers结构

Figure 3. Layers structure

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图 4 CBAM结构

Figure 4. CBAM structure

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图 5 BasicBlock 结构

Figure 5. BasicBlock structure

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图 6 不同算法增强结果及局部放大图

Figure 6. Enhancement results and local enlarged images of different algorithms

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表 1 不同算法评价指标结果

Table 1. Evaluation index results of different algorithms

场景	算法	IE	AG	SD	NIQE	SSIM	PSNR	场景	算法	IE	AG	SD	NIQE	SSIM	PSNR
1	MBLLEN	6.17	60.09	60.68	6.86	0.48	12.62	3	MBLLEN	6.69	32.1	62.65	4.74	0.55	11.36
	RUAS	4.57	41.19	83.71	6.16	0.6	15.22		RUAS	5.46	27.22	54.38	4.54	0.67	11.45
	zeroDCE	5.26	48.65	72.17	5.41	0.61	15.08		zeroDCE	5.99	38.04	49.01	3.96	0.45	15.49
	zeroDCE++	5.28	48.83	76.09	5.74	0.6	15		zeroDCE++	6.05	40.44	52.41	4.02	0.4	14.71
	Retinex−Net	6.12	63.43	61.39	4.8	0.23	12.35		Retinex−Net	6.48	51.16	47.05	4.82	0.22	10.76
	KinD++	5.73	56.52	70.77	5.15	0.59	15.18		KinD++	6.18	52.7	50.75	4.52	0.4	14.06
	本文算法	6.24	67.68	70.37	4.7	0.57	15.27		本文算法	7.32	54.56	49.65	3.88	0.56	17.5
2	MBLLEN	6.7	34.92	46.96	5.04	0.36	11.43	4	MBLLEN	7.43	56.77	64.38	3.88	0.43	11.74
	RUAS	5.23	28.59	52.28	5.07	0.7	16.84		RUAS	6.44	86.72	83.13	4.17	0.58	11.91
	zeroDCE	5.59	38.06	48.07	4.68	0.5	16.14		zeroDCE	6.82	80.49	60.01	3.72	0.49	11.86
	zeroDCE++	5.73	39.34	52.71	4.76	0.43	14.57		zeroDCE++	6.98	81.57	67.97	3.56	0.47	12.29
	Retinex−Net	6.36	55.8	47.17	4.77	0.14	11.44		Retinex−Net	7.18	116.6	53.61	4.72	0.24	10.32
	KinD++	6.02	50.15	51.35	4.65	0.46	14.3		KinD++	7.05	105.6	59.59	3.93	0.44	12.12
	本文算法	7.31	31.49	63.34	4.13	0.56	11.83		本文算法	7.71	55.7	55.7	3.29	0.28	12.78

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表 2 消融实验

Table 2. Ablation experiment

算法	IE	AG	SD	NIQE	SSIM	PSNR
算法1	6.53	71.84	52.27	4.78	0.21	11.23
算法2	6.35	62.47	56.49	4.59	0.48	13.65
算法3	7.02	60.70	62.02	4.13	0.48	13.85
本文算法	7.15	51.69	60.09	4.00	0.49	14.19

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