基于双域和ILoG−CLAHE的矿井红外图像增强算法

范伟强; 李晓宇; 翁智; 刘斌; 杨坤

doi:10.13272/j.issn.1671-251x.18033

基于双域和ILoG−CLAHE的矿井红外图像增强算法

doi: 10.13272/j.issn.1671-251x.18033

范伟强^{1, 2,},
李晓宇^{1, 2, ,},
翁智¹,
刘斌³,
杨坤⁴

1.
内蒙古大学电子信息工程学院, 内蒙古呼和浩特　010021
2.
中国矿业大学(北京) 机电与信息工程学院, 北京　100083
3.
国家能源集团宁夏煤业有限责任公司羊场湾煤矿, 宁夏银川　750408
4.
煤炭科学技术研究院有限公司, 北京　100013

基金项目: 内蒙古自治区关键技术攻关计划项目（2021GG0160，2020GG0185）；国家重点研发计划项目（2016YFC0801800）；天地科技股份有限公司科技创新资金专项项目（2021-TD-QN003）。

详细信息

作者简介:
范伟强(1992—)，男，河南渑池人，讲师，博士，现主要从事矿井监控与监视等方面的研究工作，E-mail：fan_weiqiang@163.com

通讯作者:
李晓宇(1991—)，女，内蒙古凉城人，讲师，博士，现主要从事煤矿信息化和智能化等方面的研究工作，E-mail：L_xiaoyu@126.com

中图分类号: TD67
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出版历程
- 收稿日期: 2022-09-21
- 修回日期: 2022-12-31
- 网络出版日期: 2023-01-17

Mine infrared image enhancement algorithm based on dual domain and ILoG-CLAHE

FAN Weiqiang^{1, 2
,},
LI Xiaoyu^{1, 2
, ,},
WENG Zhi¹,
LIU Bin³,
YANG Kun⁴

1.
School of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China
2.
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
3.
Yangchangwan Coal Mine, CHN Energy Ningxia Coal Industry Co., Ltd., Yinchuan 750408, China
4.
CGTEG China Coal Research Institute, Beijing 100013, China

摘要

摘要: 针对矿井复杂作业环境导致的红外图像降质，现有红外图像增强算法在实现信噪比和对比度提升的同时易丢失场景细节信息或造成目标边缘模糊的问题，提出了一种基于双域分解耦合改进的高斯−拉普拉斯（ILoG）算子和对比度受限自适应直方图均衡化（CLAHE）（ILoG−CLAHE）的矿井红外图像增强算法。首先，利用双域分解模型将矿井红外图像分解为包含高频信息的细节子图和低频信息的基础子图；其次，利用CLAHE算法对基础子图的亮度、对比度和清晰度进行提升，用以突出监视场景的概貌特征，采用构造的ILoG算子对细节子图进行噪声抑制和边缘锐化，并消除梯度反转现象；然后，通过重构处理后的基础子图和细节子图得到了图像质量改善后的重构图像；最后，设计了一种灰度重分布的Gamma校正函数，对重构图像进行亮度调整，进而得到矿井红外增强图像。通过主观视觉和客观指标对算法进行了性能分析，结果表明：经基于双域和ILoG−CLAHE的矿井红外图像增强算法增强后的矿井红外图像，整体视觉效果和客观指标均得到了较大提升，综合增强性能和鲁棒性更好。相较于原矿井红外图像和6种对比算法（CLAHE算法、双边滤波器（BF）分解与基础子图的CLAHE增强（BF−CLAHE）算法、BF分解与Gamma变换（BF−Gamma）算法、引导滤波与Gamma变换（GF−Gamma）算法、自适应直方图均衡化（AHE）耦合拉普拉斯变换（AHE−LP）算法、基于反锐化掩膜（UM）的图层融合（LF−UM）算法），该算法的综合评价指标值分别提高了0.28，0.11，0.23，0.38，0.57，0.04，0.10，图像亮度、清晰度和对比度均得到了较大提升，并且实现了噪声抑制和边缘锐化，表明该算法适用于矿井复杂作业环境中红外图像的增强处理。
- 矿井红外图像 /
- 双域分解 /
- 噪声抑制 /
- 边缘锐化 /
- 亮度调整 /
- 重构图像
Abstract: The complex working environment of mine leads to the degradation of the infrared image. The existing infrared image enhancement algorithm is easy to lose the scene details or causes the target edge blur while improving the signal-to-noise ratio and contrast. In order to solve the above problems, a mine infrared image enhancement algorithm based on dual domain decomposition coupling improved Gaussian Laplacian (ILoG) factor and contrast limited adaptive histogram equalization (CLAHE) (ILoG-CLAHE) is proposed. Firstly, the dual domain decomposition model is used to decompose the mine infrared image into a detailed sub-images containing high-frequency information and a basic sub-images containing low-frequency information. Secondly, the CLAHE algorithm is used to improve the brightness, contrast and definition of the basic sub-images to highlight the general features of the monitoring scene. The constructed ILoG operator is used to suppress noise and sharpen edges of detail sub-images and eliminate gradient inversion. Thirdly, the reconstructed image with improved image quality is obtained through the basic sub-image and detail sub-image after reconstruction processing. Finally, a Gamma correction function of gray level redistribution is designed to adjust the brightness of the reconstructed image. The mine infrared-enhanced image is obtained. The performance of the algorithm is analyzed by subjective vision and objective indicators. The results show that the overall visual effect and objective index of the mine infrared image enhanced by the mine infrared image enhancement algorithm based on dual domain and ILoG-CLAHE have been greatly improved. The comprehensive enhancement performance and robustness are better. Compared with the original mine infrared image and the six comparison algorithms, the comprehensive evaluation index values of this algorithm are increased by 0.28, 0.11, 0.23, 0.38, 0.57, 0.04, and 0.10 respectively. The six algorithms include CLAHE algorithm, bilateral filtering(BF) decomposition and CLAHE enhancement of basic sub-images (BF-CLAHE) algorithm, BF decomposition and Gamma transform (BF-Gamma) algorithm, guided filtering and Gamma transform (GF-Gamma) algorithm, adaptive histogram equalization(AHE) coupled Laplacian transform (AHE-LP) algorithm, and un-sharp mask(UM) based layer fusion (LF-UM) algorithm. The brightness, clarity and contrast of images are greatly improved, and noise suppression and edge sharpening are realized. It shows that the algorithm is suitable for the enhancement of infrared images in the complex working environment of mine.
- mine infrared image /
- dual domain decomposition /
- noise suppression /
- edge sharpening /
- brightness adjustment /
- reconstructed image

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图 1 峰值裁剪及像素点重分布过程

Figure 1. Peak clipping and pixel redistribution process

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图 2 基于双域和ILoG−CLAHE的矿井红外图像增强算法实现原理

Figure 2. Implementation principle of mine infrared image enhancement algorithm based on dual domain and improved Gaussian Laplacian (ILoG) and contrast limited adaptive histogram equalization (CLAHE)

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图 3 采煤工作面回风巷道的红外图像增强

Figure 3. Infrared image enhancement of the return air roaduay of coal mining face

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图 4 井下辅运大巷的红外图像增强

Figure 4. Infrared image enhancement of underground auxiliary transportation lane

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图 5 巷道灾害现场的红外图像增强

Figure 5. Infrared image enhancement of roadway disaster scene

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图 6 不同算法的综合评价指标值折线图

Figure 6. Line chart of comprehensive evaluation indicator of different algorithms

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图 7 本文算法综合评价指标值相较于红外图像f和对比算法的相对提高值

Figure 7. Relative improvement of the comprehensive evaluation indicator value of the proposed algorithm compared to the infrared image f and the comparison algorithms

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表 1 实验1中不同算法的客观评价指标值

Table 1. Objective evaluation indicator values of different algorithms in experiment 1

图像	评价指标
图像	Mean	MLMSE	AG	MLIE	SSIM
红外图像	42.10	17.39	8.78	2.34	—
CLAHE算法处理后图像	80.08	135.50	87.42	2.66	0.50
BF−CLAHE算法处理后图像	74.80	98.38	72.48	2.41	0.57
BF−Gamma算法处理后图像	51.15	12.41	3.06	1.41	0.96
GF−Gamma算法处理后图像	17.90	16.62	4.75	1.90	0.71
AHE−LP算法处理后图像	82.76	155.92	108.96	2.69	0.37
LF−UM算法处理后图像	71.48	146.07	107.14	2.58	0.52
本文算法处理后图像	94.31	91.36	65.30	2.64	0.58

下载: 导出CSV

表 2 实验2中不同算法的客观评价指标值

Table 2. Objective evaluation indicator values of different algorithms in experiment 2

图像	评价指标
图像	Mean	MLMSE	AG	MLIE	SSIM
红外图像	46.24	17.50	10.87	2.12	—
CLAHE算法处理后图像	77.45	109.10	83.89	2.51	0.69
BF−CLAHE算法处理后图像	71.43	65.58	38.20	1.92	0.73
BF−Gamma算法处理后图像	54.92	15.78	6.22	1.23	0.98
GF−Gamma算法处理后图像	21.74	15.73	7.35	1.67	0.74
AHE−LP算法处理后图像	77.53	128.72	71.15	2.63	0.67
LF−UM算法处理后图像	77.69	113.94	60.20	2.56	0.68
本文算法处理后图像	109.51	89.79	35.35	2.61	0.70

下载: 导出CSV

表 3 实验3中不同算法的客观评价指标值

Table 3. Objective evaluation indicator values of different algorithms in experiment 3

图像	评价指标
图像	Mean	MLMSE	AG	MLIE	SSIM
红外图像	21.97	22.39	5.92	1.45	—
CLAHE算法处理后图像	46.84	55.63	40.86	1.70	0.64
BF−CLAHE算法处理后图像	40.53	35.10	18.70	1.13	0.72
BF−Gamma算法处理后图像	27.53	19.65	3.77	0.81	0.97
GF−Gamma算法处理后图像	9.43	27.05	4.69	0.97	0.72
AHE−LP算法处理后图像	50.80	81.73	46.80	2.59	0.55
LF−UM算法处理后图像	46.17	71.15	59.27	1.78	0.64
本文算法处理后图像	61.90	57.41	22.28	2.63	0.66

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表 4 不同算法的综合评价指标值

Table 4. Comprehensive evaluation indictor values of different algorithms

场景	综合评价指标值
场景	f	CLAHE算法	BF−CLAHE算法	BF−Gamma算法	GF−Gamma算法	AHE−LP算法	LF−UM算法	本文算法
实验1	0.52	0.71	0.62	0.34	0.24	0.71	0.70	0.72
实验2	0.49	0.63	0.42	0.33	0.13	0.64	0.60	0.65
实验3	0.41	0.51	0.35	0.32	0.13	0.66	0.59	0.68
随机实验	0.45	0.62	0.50	0.35	0.16	0.69	0.63	0.73

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