| Citation: |
WANG Yuanbin，GUO Yaru，LIU Jia，et al. Low illumination image enhancement algorithm of CycleGAN coal mine based on attention mechanism and Dilated convolution[J]. Coal Science and Technology，2024，52（S2）：375−383. DOI: 10.12438/cst.2023-1597
|
The complex underground environment, filled with a large amount of dust and water vapor, and uneven illumination of artificial light source, leads to problems such as low illumination and loss of detail features in images collected by underground monitoring equipment, which seriously affects the real-time performance of mining safety monitoring equipment, is not good for subsequent computer vision tasks, and it is difficult to collect underground data. It is difficult to make paired low-light image data sets for model training. To solve these problems, a low illumination image enhancement algorithm based on CycleGAN is proposed. In view of the difficulty of collecting paired data set under mine, CycleGAN network is used for unsupervised learning. In order to improve the detail feature extraction ability of the generator network, the image enhancement network was constructed by using the Parameter-Free Attention Mechanism (simAM) and the Dual-Channel Attention Mechanism (CBAM) to improve the anti-interference ability of the model in complex background, so that the model could recover the image detail features better, which improved the anti-interference ability of the model under complex background and made the model recover the detail features better. A luminance enhancement module based on residual cavity convolution is introduced to increase the luminance of the image while enlarging the receptive field of the generator network, which is conducive to detail recovery and visual quality improvement. Patch-GAN is apply for the discriminator of the network, and the input is mapped into a matrix to pay more comprehensive attention to the details of different regions of the image, and improve the discriminator's resolution of image details. Experimental results show that compared with the CycleGAN algorithm, the proposed method improves the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), information entropy and visual information fidelity (VIF) by 11.31%, 8.07%, 2.58% and 6.18% on average.
| [1] |
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.
|
| [2] |
贺磊,魏明生,仇欣宇,等. 基于UWB的井下人员定位算法研究[J]. 工矿自动化,2022,48(6):134−138.
HE Lei,WEI Mingsheng,QIU Xinyu,et al. Research on positioning algorithm of underground personnel based on UWB[J]. Journal of Mine Automation,2022,48(6):134−138.
|
| [3] |
田文洪,曾柯铭,莫中勤,等. 基于卷积神经网络的驾驶员不安全行为识别[J]. 电子科技大学学报,2019,48(3):381−387.
TIAN Wenhong,ZENG Keming,MO Zhongqin,et al. Recognition of unsafe driving behaviors based on convolutional neural network[J]. Journal of University of Electronic Science and Technology of China,2019,48(3):381−387.
|
| [4] |
XU Dawen,LIU Yong. Reversible data hiding in H. 264/AVC videos based on hybrid-dimensional histogram modification[J]. Multimedia Tools and Applications,2022,81(20):29305−29319. doi: 10.1007/s11042-022-12740-3
|
| [5] |
PAUL A. Adaptive tri-plateau limit tri-histogram equalization algorithm for digital image enhancement[J]. The Visual Computer,2023,39(1):297−318. doi: 10.1007/s00371-021-02330-z
|
| [6] |
于林泉,陆军. 复杂背景下的人眼状态与虹膜位置检测[J]. 哈尔滨工程大学学报,2023,44(4):594−605.
YU Linquan,LU Jun. Human eye state and iris position detection in a complex background[J]. Journal of Harbin Engineering University,2023,44(4):594−605.
|
| [7] |
王满利,田子建. 基于非下采样轮廓波变换的矿井图像增强算法[J]. 煤炭学报,2020,45(9):3351−3362.
WANG Manli,TIAN Zijian. Mine image enhancement algorithm based on nonsubsampled contourlet transform[J]. Journal of China Coal Society,2020,45(9):3351−3362.
|
| [8] |
王满利,张航,李佳悦,等. 基于深度神经网络的煤矿井下低光照图像增强算法[J]. 煤炭科学技术,2023,51(9):231−241. doi: 10.12438/cst.2022-1626
WANG Manli,ZHANG Hang,LI Jiayue,et al. Deep neural network-based image enhancement algorithm for low-illumination images underground coal mines[J]. Coal Science and Technology,2023,51(9):231−241. doi: 10.12438/cst.2022-1626
|
| [9] |
吴一全,史骏鹏. 基于多尺度Retinex的非下采样Contourlet域图像增强[J]. 光学学报,2015,35(3):87−96.
WU Yiquan,SHI Junpeng. Image enhancement in non-subsampled contourlet transform domain based on multi-scale retinex[J]. Acta Optica Sinica,2015,35(3):87−96.
|
| [10] |
江兴方,王戈,沈为民. 一种色彩改进型Retinex彩色图像增强方法[J]. 光电子. 激光,2008,19(10):1402−1404.
JIANG Xingfang,WANG Ge,SHEN Weimin. A method of color image enhancement using color advanced retinex[J]. Journal of Optoelectronics. Laser,2008,19(10):1402−1404.
|
| [11] |
LORE K G,AKINTAYO A,SARKAR S. LLNet:A deep autoencoder approach to natural low-light image enhancement[J]. Pattern Recognition,2017,61:650−662. doi: 10.1016/j.patcog.2016.06.008
|
| [12] |
WU Dongmei,ZHANG Siqi. Research on image enhancement algorithm of coal mine dust[C]//2018 International Conference on Sensor Networks and Signal Processing (SNSP). Piscataway,NJ,USA. IEEE,2018:261−265.
|
| [13] |
LYU Feifan,LU Feng,WU Jianhua,et al. MBLLEN:low-light image video enhancement using CNNs[C]//Proceedings of BMVC’18. Washington,DC,USA. BMVC,2018:220−226.
|
| [14] |
GUO Xiaojie,LI Yu,LING Haibin. LIME:Low-light image enhancement via illumination map estimation[J]. IEEE Transactions on Image Processing,2017,26(2):982−993. doi: 10.1109/TIP.2016.2639450
|
| [15] |
刘超,张晓晖. 超低照度下微光图像的深度卷积自编码网络复原[J]. 光学 精密工程,2018,26(4):951−961. doi: 10.3788/OPE.20182604.0951
LIU Chao,ZHANG Xiaohui. Deep convolutional autoencoder networks approach to low-light level image restoration under extreme low-light illumination[J]. Optics and Precision Engineering,2018,26(4):951−961. doi: 10.3788/OPE.20182604.0951
|
| [16] |
TAO Li,ZHU Chuang,XIANG Guoqing,et al. LLCNN:A convolutional neural network for low-light image enhancement[C]//2017 IEEE Visual Communications and Image Processing (VCIP). Piscataway,NJ,USA. IEEE,2017:1−4.
|
| [17] |
GOODFELLOW I J,POUGET-Abadie J,MIRZA M,et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge,MA,USA. MIT Press,2014:2672−2680.
|
| [18] |
ZHANG He,SINDAGI V,PATEL V M. Image de-raining using a conditional generative adversarial network[J]. IEEE Transactions on Circuits and Systems for Video Technology,2020,30(11):3943−3956. doi: 10.1109/TCSVT.2019.2920407
|
| [19] |
ISOLA P,ZHU Junyan,ZHOU Tinghui,et al. Image-to-image translation with conditional adversarial networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway,NJ,USA. IEEE,2017:5967−5976.
|
| [20] |
JIANG Yifan,GONG Xinyu,LIU Ding,et al. EnlightenGAN:Deep light enhancement without paired supervision[J]. IEEE Transactions on Image Processing,2021,30:2340−2349. doi: 10.1109/TIP.2021.3051462
|
| [21] |
ZHU Junyan,PARK T,ISOLA P. Unpaired image-to-image translation using cycle-consistent adversarial networks [C]//2017 IEEE International Conference on Computer Vision (ICCV). Piscataway,NJ,USA. IEEE,2017:2223−2232.
|
| [22] |
阮顺领,李少博,顾清华,等. 基于双向特征融合的露天矿区道路障碍检测[J]. 煤炭学报,2023,48(3):1425−1438.
RUAN Shunling,LI Shaobo,GU Qinghua,et al. Road obstacle detection in open-pit mines based on bidirectional feature fusion[J]. Journal of China Coal Society,2023,48(3):1425−1438.
|
| [23] |
吴佳奇,张文琪,陈伟,等. 基于改进CycleGAN的煤矿井下低照度图像增强方法[J]. 华中科技大学学报(自然科学版),2023,51(5):40−46.
WU Jiaqi,ZHANG Wenqi,CHEN Wei,et al. Image enhancement method of underground low illumination in coal mine based on improved CycleGAN[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition),2023,51(5):40−46.
|
| [1] | LIU Chengyong, GU Wenzhe, CUI Feng, ZHANG Yun, YANG Yanbin, QIU Fengqi, YUAN Chaofeng, CHENG Haixing, LI Ming. Research progress and prospects on resource utilization of coal gangue for soil remediation in China[J]. COAL SCIENCE AND TECHNOLOGY, 2025, 53(6): 125-140. DOI: 10.12438/cst.2025-0335 |
| [2] | GU Xiaowei, GE Xiaowei, LIU Shaoqian, DING Xuecheng, WANG Li, HUANG Dongwei, WANG Dawei, MA Yile. Research progress on physical and chemical properties of coal gasification slag and its preparation of building materials[J]. COAL SCIENCE AND TECHNOLOGY, 2025, 53(6): 29-45. DOI: 10.12438/cst.2025-0665 |
| [3] | FAN Yongyong, YANG Wenfu, QI Fuhui, ZHANG Xingxing, SONG Wenlong, SU Yufei, XIE Hua. Diversified and efficient utilization and evaluation of remaining coal resources of closed coal mines in Shanxi Province[J]. COAL SCIENCE AND TECHNOLOGY, 2025, 53(4): 114-124. DOI: 10.12438/cst.2024-1352 |
| [4] | WU Jinwen, DENG Xiaowei, JIAO Feishuo, LYU Bo, FANG Chaojun. Resource utilization status and development trend of bulk solid waste of coal-based ash/slag[J]. COAL SCIENCE AND TECHNOLOGY, 2024, 52(6): 238-252. DOI: 10.12438/cst.2023-1102 |
| [5] | ZHU Tao, WU Xinjuan, XING Cheng, JU Qiuge, SU Siyuan. Current situation and progress of coal gangue resource utilization[J]. COAL SCIENCE AND TECHNOLOGY, 2024, 52(1): 380-390. DOI: 10.12438/cst.2023-1917 |
| [6] | HE Xuwen, WANG Shaozhou, ZHANG Xuewei, LI Fuqin. Coal mine drainage resources utilization technology innovation[J]. COAL SCIENCE AND TECHNOLOGY, 2023, 51(1): 523-530. DOI: 10.13199/j.cnki.cst.2022-1641 |
| [7] | QIN Shenjun, XU Fei, CUI Li, WANG Jinxi, LI Shenyong, ZHAO Zesen, XIAO Lin, GUO Yanxia, ZHAO Cunliang. Geochemistry characteristics and resource utilization of strategically critical trace elements from coal-related resources[J]. COAL SCIENCE AND TECHNOLOGY, 2022, 50(3): 1-38. |
| [8] | YAN Lihuang, WANG Wenfeng, BIAN Zhengfu. Prospectsof resource utilization and disposal of coal-based solid wastes in Xinjiang[J]. COAL SCIENCE AND TECHNOLOGY, 2021, 49(1): 319-330. DOI: 10.13199/j.cnki.cst.2021.01.030 |
| [9] | HE Xuwen, ZHANG Xiaohang, LI Fuqin, ZHANG Chunhui. Comprehensive utilization system and technical innovation of coal mine water resources[J]. COAL SCIENCE AND TECHNOLOGY, 2018, (9). |
| [10] | Analysis and Utilization Direction on Associated Resources of Lignite in China[J]. COAL SCIENCE AND TECHNOLOGY, 2012, (12). |
| 1. |
高春雷, 侯竣升, 丁子涵, 黄磊, 张浩, 田佰起, 吴俊杰, 郝南京. 基于煤气化渣的Fe_2O_3纳米流体制备与电池热管理试验. 煤炭科学技术. 2025(06)
 本站查看
| |
| 2. |
顾晓薇, 葛晓伟, 刘少迁, 丁学成, 王莉, 黄冬蔚, 王达伟, 马以勒. 煤气化渣理化特性及其制备建筑材料研究进展. 煤炭科学技术. 2025(06)
本站查看
| |
| 3. |
张钦礼, 陶云波, 冯岩, 陈秋松, 高凌志, 张芋杰, 王道林. 采-选-冶-化固废基充填材料制备及综合性能研究综述. 煤炭科学技术. 2025(06)
本站查看
| |
| 4. |
张翠敏, 王之敏, 王浩然, 李红真, 李万磊, 黄京立, 张秀芝. 全固废碱激发胶凝材料混凝土砖的制备及性能研究. 混凝土与水泥制品. 2025(06)
| |
| 5. |
孙浩, 蒋林亮, 常新卓, 田全志. 煤气化渣免烧陶粒滤料强度和孔结构的调控. 煤炭科学技术. 2025(06)
本站查看
| |
| 6. |
丁天昱，王龙江，鱼涛，陈刚. 煤气化渣的改性及其在水泥增强中的应用研究. 化工技术与开发. 2025(Z1): 66-69 .
| |
| 7. |
李飞，王福宁，金涛，朱建华，刘向辉. 大宗煤基固废利用研究现状与展望. 山东化工. 2025(01): 75-78 .
| |
| 8. |
阿拉腾沙嘎，段乐乐. 煤气化渣资源化利用的研究进展. 化工技术与开发. 2025(03): 44-47 .
| |
| 9. |
廖昌建，王晶，金平，刘志禹，徐婉怡，王坤. 煤气化渣理化特性及其所含重金属迁移规律综述. 煤炭科学技术. 2025(02): 426-443 .
本站查看
| |
| 10. |
孙振杰，唐彪，董开明，吴松涛，尤晓东，黄潇洋，刘元，张浩，田佰起，郭飞强. 煤气化细渣活化耦合酸浸过程结构演化特性. 中国矿业大学学报. 2025(03): 524-533 .
| |
| 11. |
张吉雄，顾晓薇，周楠，于广锁，郝南京，肖耀猛. 煤气化灰渣规模化分质梯级利用与高效处置技术研究. 中国矿业大学学报. 2025(03): 471-484 .
| |
| 12. |
周柏屹，王浩南，夏良志. 气化渣破碎与预干燥过程数值仿真及实验研究. 现代化工. 2024(S2): 359-365 .
| |
| 13. |
杨帆. 煤气化过程中副产物气化渣的综合利用技术研究. 化工管理. 2024(31): 90-93 .
| |
| 14. |
周季龙，王亚茹，魏怡菲，孙建平，孙伟，岳彤. 气化渣酸浸液去除铁铬及聚合氯化铝制备. 中国有色金属学报. 2024(12): 4111-4125 .
| |
| 15. |
程臻赟，王占银，傅博，雷华，苏梦，丁楠，陈美庆. 煤气化渣黏土复合地基填料力学特性研究. 江西建材. 2024(10): 217-220 .
| |
| 16. |
傅博，程臻赟，雷华. 助磨剂对气化渣球磨特性的影响. 化学反应工程与工艺. 2024(06): 516-520+539 .
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: