A dehazing and enhancement algorithm for heterogeneous images of underground mining environments in coal mines

Authors

ZHANG Xuhui, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi’an 710054, ChinaFollow
XIE Yanbin, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
YANG Wenjuan, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi’an 710054, China
ZHANG Chao, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
WAN Jicheng, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
DONG Zheng, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
WANG Yanqun, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
JIANG Jie, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
LI Long, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Abstract

Objective In coal mines, the uneven distribution of dust haze and complex illumination conditions caused by underground coal mining and dust removal lead to blurred video images, as well as the loss of information and details. Hence, this study proposed a dehazing and enhancement algorithm for heterogeneous images of underground mining environments. Methods Initially, hazy images were segmented into zones with different brightness values, for which the average ambient light intensity of global dark channels was calculated. The calculation results were integrated through weighting with the ambient light of local bright channels, which was obtained using adaptive gamma correction and multiscale Gaussian filtering. Consequently, accurate ambient light intensity estimates were determined. To preserve image details while achieving natural dehazing effects, transmission maps were processed using multiscale fusion correction technology and were then refined using joint bilateral filtering. Afterward, clear hazy images were obtained using the atmospheric scattering model. To further enhance the overall brightness and contrast of the dehazed images, white balance correction was performed. Specifically, images were converted into the hue-saturation-value (HSV) color space. Then, the details and contrast of images were enhanced using the proposed adaptive saturation correction and improved contrast enhancement algorithm, as well as Laplacian sharpening. Results and Conclusions Images of typical, actual scenarios were processed using five algorithms: dark channel prior (DCP), maximum reflectance prior (MRP), optimal-scale fusion-based dehazing (OSFD), multiscale fusion – low light image enhancement (MF-LIME), and contrast enhancement and exposure fusion (CEEF). The processing results of these algorithms were those of the proposed algorithm based on multiple indicators. The results indicate that compared to the above novel and excellent algorithms in terms of their optimal indicators, the proposed algorithm exhibited that: (1) The average gradients were approximately twice those obtained by CEEF, suggesting elevated image clarity. (2) The average information entropy decreased by approximately 1% compared to that of MRP, implying more information preserved. (3) The standard deviation increased by approximately 6% on average compared to OSFD, representing improved image contrast. (4) The average fog aware density evaluator (FADE) value by approximately 23% compared to CEEF, implying an effective reduction in the haze Therefore, the proposed algorithm can effectively improve the visual effects and quality of blurred images of underground mining environments in coal mines, exhibiting high utility in engineering.

Keywords

regional segmentation, dark-light channel fusion, contrast enhancement, white balance correction, adaptive saturation correction, mining operations

DOI

10.12363/issn.1001-1986.24.09.0602

Recommended Citation

ZHANG Xuhui, XIE Yanbin, YANG Wenjuan, et al. (2025) "A dehazing and enhancement algorithm for heterogeneous images of underground mining environments in coal mines," Coal Geology & Exploration: Vol. 53: Iss. 1, Article 21.
DOI: 10.12363/issn.1001-1986.24.09.0602
Available at: https://cge.researchcommons.org/journal/vol53/iss1/21

Reference

[1] 魏文艳. 综采工作面智能化开采技术发展现状及展望[J]. 煤炭科学技术，2022，50(增刊2)：244−253.

WEI Wenyan. Development status and prospect of intelligent mining technology of longwall mining[J]. Coal Science and Technology，2022，50(Sup.2)：244−253.

[2] 程德强，钱建生，郭星歌，等. 煤矿安全生产视频AI识别关键技术研究综述[J]. 煤炭科学技术，2023，51(2)：349−365.

CHENG Deqiang，QIAN Jiansheng，GUO Xingge，et al. Review on key technologies of AI recognition for videos in coal mine[J]. Coal Science and Technology，2023，51(2)：349−365.

[3] LIU Zhigang，CAO Anye，GUO Xiaosheng，et al. Deep-hole water injection technology of strong impact tendency coal seam：A case study in Tangkou Coal Mine[J]. Arabian Journal of Geosciences，2018，11(2)：12.

[4] 郭志杰，南柄飞，王凯. 煤矿井下视频雾浓度检测及实时去雾方法[J]. 工矿自动化，2023，49(3)：31−38.

GUO Zhijie，NAN Bingfei，WANG Kai. Research and application of video fog concentration detection and real-time fog removal method in underground coal mine[J]. Journal of Mine Automation，2023，49(3)：31−38.

[5] 王国法. 煤矿智能化最新技术进展与问题探讨[J]. 煤炭科学技术，2022，50(1)：1−27.

WANG Guofa. New technological progress of coal mine intelligence and its problems[J]. Coal Science and Technology，2022，50(1)：1−27.

[6] 谢勇，贾惠珍，王同罕，等. 图像去雾算法综述[J]. 计算机与数字工程，2022，50(12)：2765−2774.

XIE Yong，JIA Huizhen，WANG Tonghan，et al. Overview of image defogging algorithms[J]. Computer & Digital Engineering，2022，50(12)：2765−2774.

[7] YU Zhe，SUN Bangyong，LIU Di，et al. STRASS dehazing：Spatio-temporal retinex-inspired dehazing by an averaging of stochastic samples[J]. Journal of Renewable Materials，2022，10(5)：1381−1395.

[8] XU Zhiyuan，LIU Xiaoming，JI Na. Fog removal from color images using contrast limited adaptive histogram equalization[C]//2009 2nd International Congress on Image and Signal Processing. Tianjin，China. IEEE，2009：1–5.

[9] FANG Faming，LI Fang，YANG Xiaomei，et al. Single image dehazing and denoising with variational method[C]//2010 International Conference on Image Analysis and Signal Processing. Zhejiang. IEEE，2010：219–222.

[10] JIAN Muwei，LIU Xiangyu，LUO Hanjiang，et al. Underwater image processing and analysis：A review[J]. Signal Processing：Image Communication，2021，91：116088.

[11] 张立亚，郝博南，孟庆勇，等. 基于HSV空间改进融合Retinex算法的井下图像增强方法[J]. 煤炭学报，2020，45(增刊1)：532−540.

ZHANG Liya，HAO Bonan，MENG Qingyong，et al. Method of image enhancement in coal mine based on improved retex fusion algorithm in HSV space[J]. Journal of China Coal Society，2020，45(Sup.1)：532−540.

[12] 苏波，李超，王莉. 基于多权重融合策略的Retinex矿井图像增强算法[J]. 煤炭学报，2023，48(增刊2)：813−822.

SU Bo，LI Chao，WANG Li. Mine image enhancement algorithm based on retinex using multi-weight fusion strategy[J]. Journal of China Coal Society，2023，48(Sup.2)：813−822.

[13] CAI Bolun，XU Xiangmin，JIA Kui，et al. DehazeNet：An end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing，2016，25(11)：5187−5198.

[14] LI Yunan，MIAO Qiguang，OUYANG Wanli，et al. LAP-net：Level-aware progressive network for image dehazing[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul，Korea (South). IEEE，2019：3275–3284.

[15] TU Zhengzhong，TALEBI H，ZHANG Han，et al. MAXIM：Multi-axis MLP for image processing[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans，LA，USA. IEEE，2022：5759–5770.

[16] 王媛彬，郭亚茹，刘佳，等. 基于注意力机制和空洞卷积的CycleGAN煤矿井下低照度图像增强算法[J/OL]. 煤炭科学技术：1-10[2024-01-23]. https://link.cnki.net/urlid/11.2402.TD.20240119.1728.013

WANG Yuanbin，GUO Yaru，LIU Jia，et al. Low illumination image enhancement algorithm of CycleGAN coal mine based on atten-tion mechanism and Dilated convolution[J/OL]. Coal Science and Technology：1-10[2024-01-23]. https://link.cnki.net/urlid/11.2402.TD.20240119.1728.013

[17] MCCARTNEY E J，HALL F F. Optics of the atmosphere：Scattering by molecules and particles[J]. 1977，30(5)：76–77.

[18] HE Kaiming，SUN Jian，TANG Xiaoou. Single image haze removal using dark channel prior[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami，FL，USA. IEEE，2009：1956–1963.

[19] HE Kaiming，SUN Jian，TANG Xiaoou. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2013，35(6)：1397−1409.

[20] 吴开兴，张琳，李丽宏. 煤矿井下雾尘图像清晰化算法[J]. 工矿自动化，2018，44(3)：70−75.

WU Kaixing，ZHANG Lin，LI Lihong. Sharpening algorithm for underground images with fog and dust[J]. Industry and Mine Automation，2018，44(3)：70−75.

[21] 赵猛，任志浩，褚海峰，等. 基于大气散射模型的采煤工作面尘雾图像清晰化算法[J]. 煤炭学报，2023，48(8)：3312−3322.

ZHAO Meng，REN Zhihao，CHU Haifeng，et al. Dust and fog image-sharpening algorithm based on atmospheric scattering model in coal face[J]. Journal of China Coal Society，2023，48(8)：3312−3322.

[22] 张旭辉，杨红强，白琳娜，等. 煤矿掘进工作面低照度视频增强技术研究[J]. 煤田地质与勘探，2023，51(1)：309−316.

ZHANG Xuhui，YANG Hongqiang，BAI Linna，et al. Research on low illumination video enhancement technology in coal mine heading face[J]. Coal Geology & Exploration，2023，51(1)：309−316.

[23] 张旭辉，麻兵，杨文娟，等. 煤矿井下非均匀照度图像去噪研究[J]. 工矿自动化，2024，50(2)：1−8.

ZHANG Xuhui，MA Bing，YANG Wenjuan，et al. Research on denoising of uneven lighting images in coal mine underground[J]. Journal of Mine Automation，2024，50(2)：1−8.

[24] PANAGOPOULOS A，WANG Chaohui，SAMARAS D，et al. Estimating shadows with the bright channel cue[M]//KUTULAKOS K N. ，ed. Lecture notes in computer science. Heidelberg：Springer Berlin Heidelberg，2012：1–12.

[25] RUBEL O，LUKIN V，RUBEL A，et al. Selection of lee filter window size based on despeckling efficiency prediction for sentinel SAR images[J]. Remote Sensing，2021，13(10)：1887.

[26] CHOI L K，YOU J，BOVIK A C. Referenceless prediction of perceptual fog density and perceptual image defogging[J]. IEEE Transactions on Image Processing，2015，24(11)：3888−3901.

[27] 周辉奎，章立，胡素娟. 改进直方图匹配和自适应均衡的水下图像增强[J]. 红外技术，2024，46(5)：532−538.

ZHOU Huikui，ZHANG Li，HU Sujuan. Underwater image enhancement based on improved histogram matching and adaptive equalization[J]. Infrared Technology，2024，46(5)：532−538.

[28] PISANO E D，ZONG Shuquan，HEMMINGER B M，et al. Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms[J]. Journal of Digital Imaging，1998，11(4)：193.

[29] SHI Zhenghao，FENG Yaning，ZHAO Minghua，et al. Normalised gamma transformation-based contrast-limited adaptive histogram equalisation with colour correction for sand–dust image enhancement[J]. IET Image Processing，2020，14(4)：747−756.

[30] ZHANG Xingchen. Benchmarking and comparing multi-exposure image fusion algorithms[J]. Information Fusion，2021，74：111−131.

[31] ZHANG Jing，CAO Yang，FANG Shuai，et al. Fast haze removal for nighttime image using maximum reflectance prior[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu，HI，USA. IEEE，2017：7016–7024.

[32] ZHANG Jing，CAO Yang，ZHA Zhengjun，et al. Nighttime dehazing with a synthetic benchmark[C]//Proceedings of the 28th ACM International Conference on Multimedia. Seattle WA USA. ACM，2020.

[33] ANCUTI C O，ANCUTI C，DE VLEESCHOUWER C，et al. Color balance and fusion for underwater image enhancement[J]. IEEE Transactions on Image Processing，2018，27(1)：379−393.

[34] LIU Xiaoning，LI Hui，ZHU Ce. Joint contrast enhancement and exposure fusion for real-world image dehazing[J]. IEEE Transactions on Multimedia，2022，24：3934−3946.

[35] ZHANG Wenhao，LI Ge，YING Zhenqiang. A new underwater image enhancing method via color correction and illumination adjustment[C]//2017 IEEE Visual Communications and Image Processing (VCIP). St. Petersburg，FL，USA. IEEE，2017：1–4.

[36] 王效灵，胡志杰，徐帅帅，等. 改进暗通道先验和策略性融合的图像去雾算法[J]. 计算机工程，2023，49(10)：212−221.

WANG Xiaoling，HU Zhijie，XU Shuaishuai，et al. Image dehazing algorithm using improved dark channel prior and strategic fusion[J]. Computer Engineering，2023，49(10)：212−221.