Image Enhancement Method in Underground Coal Mines Based on an Improved Particle Swarm Optimization Algorithm
Source abstract
Abstract
Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image Enhancement Techniques · Advanced Image Fusion Techniques · Video Surveillance and Tracking Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Smart Materials and Applied Technology, Lianyungang Normal College, Lianyungang 222006, China
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 211006, China
Funding
National Natural Science Foundation of China · Independent Innovation Project of “Double-First Class” Construction of China University of Mining and Technology · Natural Science Foundation of Jiangsu Province · Jiangsu Funding Program for Excellent Postdoctoral Talent · China Postdoctoral Science Foundation · Qing Lan project for excellent teaching team of Jiangsu province (2022) · Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
References (33)
Show source-provided references
1. Wang, J.F., Feng, L.J., Zhao, Z., and Yu, H.J. (2011, January 22–23). Research on Underground Coal Mine Production Process Anlysis and Informatization. Proceedings of the International Conference on Engineering Materials, Energy, Management and Control, Beijing, China.
2. Zhong, T., Lou, P.J., Ruan, H.X., and Zhang, B. (2011, January 13–16). Construction of Coal Mine Comprehensive Informatization Based on Kilomega Fiber-optic Industry Ether Ring Network. Proceedings of the International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation (CDMMS 2011), Hangzhou, China.
3. Yu, K., Zhou, L.J., Liu, P.P., Chen, J., Miao, D.J., and Wang, J.S. (2022). Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management. Mathematics, 10.10.3390/math10214028 ↗
4. Adaptive image enhancement algorithm based on the model of surface roughness detection system10.1186/s13640-018-0343-1 ↗
5. Dim and Small Target Detection Based on Spiral Gradient Optimization Estimation and High-Order Correlation Enhancement10.1109/ACCESS.2022.3147460 ↗
6. Gesture recognition based on skeletonization algorithm and CNN with ASL database10.1007/s11042-018-6748-0 ↗
7. Probability analysis for grasp planning facing the field of medical robotics10.1016/j.measurement.2019.03.010 ↗
8. Cheng, Y., and Li, B. (2021, January 14–16). Image Segmentation Technology and Its Application in Digital Image Processing. Proceedings of the 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), Dalian, China.10.1109/IPEC51340.2021.9421206 ↗
9. A Comprehensive Overview of Image Enhancement Techniques10.1007/s11831-021-09587-6 ↗
10. Numerical simulation of thermal insulation and longevity performance in new lightweight ladle10.1002/cpe.5830 ↗
11. Deep learning on image denoising: An overview10.1016/j.neunet.2020.07.025 ↗
12. Image denoising review: From classical to state-of-the-art approaches10.1016/j.inffus.2019.09.003 ↗
13. Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement10.1016/j.cpet.2021.06.005 ↗
14. Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution10.1109/TIP.2012.2226047 ↗
15. Global brightness and local contrast adaptive enhancement for low illumination color image10.1016/j.ijleo.2013.09.051 ↗
16. Tripartite sub-image histogram equalization for slightly low contrast gray-tone image enhancement10.1016/j.patcog.2022.109043 ↗
17. Liu, C.W., Sui, X.B., Kuang, X.D., Liu, Y., Gu, G.H., and Chen, Q. (2019). Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification. Remote Sens., 11.10.3390/rs11070849 ↗
18. Wu, D.M., Zhang, S.Q., and IEEE (2018, January 28–31). Research on Image Enhancement Algorithm of Coal Mine Dust. Proceedings of the 1st Annual International Conference on Sensor Networks and Signal Processing (SNSP), Xi’an, China.10.1109/SNSP.2018.00057 ↗
19. An Adaptive Recovering Algorithm for The Color Bar Code Image Based on Gamma Transforms10.1088/1742-6596/1952/2/022029 ↗
20. A fuzzy and spline based dynamic histogram equalization for contrast enhancement of brain images10.1002/ima.22483 ↗
21. Low-illumination image enhancement algorithm based on multi-feature fusion10.1088/1757-899X/799/1/012038 ↗
22. Image Enhancement Based on Discrete Cosine Transforms (DCT) and Discrete Wavelet Transform (DWT): A Review10.1088/1757-899X/557/1/012027 ↗
23. Banerjee, A., Shivakumara, P., Pal, S., Pal, U., and Liu, C.L. (2021, January 9–12). DCT-DWT-FFT Based Method for Text Detection in Underwater Images. Proceedings of the 6th Asian Conference on Pattern Recognition (ACPR), Jeju Island, Republic of Korea.10.1007/978-3-031-02444-3_16 ↗
24. A nighttime image enhancement method based on Retinex and guided filter for object recognition of apple harvesting robot10.1177/1729881417753871 ↗
25. Low-Light Image Enhancement by Principle Component Analysis10.1109/ACCESS.2018.2887296 ↗
26. Rahman, Z., Aamir, M., Pu, Y.F., Ullah, F., and Dai, Q. (2018). A Smart System for Low-Light Image Enhancement with Color Constancy and Detail Manipulation in Complex Light Environments. Symmetry, 10.10.3390/sym10120718 ↗
27. Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization10.3389/fbioe.2022.865820 ↗
28. Low-light image enhancement based on membership function and gamma correction10.1007/s11042-021-11505-8 ↗
29. Identity-Expression Dual Branch Network for Facial Expression Recognition10.1109/TCDS.2020.3034807 ↗
30. An image contrast enhancement algorithm for grayscale images using particle swarm optimization10.1007/s11042-018-5650-0 ↗
31. Shi, Y., and Eberhart, R. (1998, January 4–9). A modified particle swarm optimizer. Proceedings of the 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), Anchorage, AK, USA.
32. David, D., and IEEE (2015, January 3–5). Low Illumination Image Enhancement Algorithm Using Iterative Recursive Filter And Visual Gamma Transformation Function. Proceedings of the Fifth International Conference on Advances in Computing and Communications (ICACC), Kochi, India.10.1109/ICACC.2015.43 ↗
33. An analytical solution for the Caputo type generalized fractional evolution equation10.1016/j.aej.2021.10.055 ↗