SF6 Leakage Region Enhancement Algorithm Based on Improved HE

LU Quan; HUANG Lifeng; HU Mengzhu

Volume 46 Issue 4

Apr. 2024

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LU Quan, HUANG Lifeng, HU Mengzhu. SF6 Leakage Region Enhancement Algorithm Based on Improved HE[J]. Infrared Technology , 2024, 46(4): 437-442.

Citation:

LU Quan, HUANG Lifeng, HU Mengzhu. SF6 Leakage Region Enhancement Algorithm Based on Improved HE[J]. Infrared Technology , 2024, 46(4): 437-442.

LU Quan, HUANG Lifeng, HU Mengzhu. SF6 Leakage Region Enhancement Algorithm Based on Improved HE[J]. Infrared Technology , 2024, 46(4): 437-442.

Citation:

LU Quan, HUANG Lifeng, HU Mengzhu. SF6 Leakage Region Enhancement Algorithm Based on Improved HE[J]. Infrared Technology , 2024, 46(4): 437-442.

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SF6 Leakage Region Enhancement Algorithm Based on Improved HE

1.
School of Electrical Engineering, Guangxi University, Nanning 530004, China
2.
Electric Power Science Research Institute, Guangxi Power Grid Co. Ltd., Nanning 530004, China

Received Date: 2022-03-16
Rev Recd Date: 2022-04-20
Publish Date: 2024-04-20

Abstract

Abstract

Infrared imaging of SF6 gas is easily affected by environmental noise and exhibits low contrast and signal-to-noise ratio. As a result, existing algorithms cannot adaptively enhance the SF6 leakage area or suppress Gaussian noise. Therefore, this study proposes an improved HE-based SF6 leakage-area enhancement algorithm. The algorithm first uses SSR to process the original SF6 image to obtain the reflection image R, and then uses guided filtering to decompose the reflection image R into detail and base layers. Finally, an improved histogram equalization is used to adaptively process the base layer, and the enhanced images are fused to obtain the final image. The experiment results demonstrate that the proposed algorithm can not only adaptively enhance the contrast of the leaked area but also has good edge preservation and Gaussian noise suppression performance. Its enhancement effect is superior to that of the existing SF6 infrared image enhancement algorithm. This effectively improves the low-contrast and low signal-to-noise ratio characteristics of the SF6 infrared images.
- single-scale-retinex,
- guide filtering,
- SF6 infrared image,
- histogram equalization

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References

[1]	ZHANG X, XIAO H, HU X, et al. Effects of reduced electric field on sulfur hexafluoride removal for a double dielectric barrier discharge reactor[J]. IEEE Transactions on Plasma Science, 2018, 46(3): 563-570. doi: 10.1109/TPS.2018.2796134
[2]	郭瑛, 周会高. 高压开关行业热点分析[J]. 电气时代, 2019, 1(6): 20-24. https://www.cnki.com.cn/Article/CJFDTOTAL-DQSD201906007.htm GUO Ying, ZHOU Huigao. Hot spot analysis of high-voltage switch industry[J]. Electric Age, 2019, 1(6): 20-24. https://www.cnki.com.cn/Article/CJFDTOTAL-DQSD201906007.htm
[3]	LIU C, GU W, SHI L, et al. A method to construct early-warning and emergency response system for sulfur hexafluoride leakage in substations[J]. IEEE Access, 2020(8): 47082-47091.
[4]	季怡萍, 邓先钦, 徐鹏, 等. SF6气体泄漏红外成像检测的技术分析和应用探讨[J]. 红外技术, 2022, 44(2): 198-204. http://hwjs.nvir.cn/article/id/54e422ef-acce-4c36-9dbc-66801c99d8be JI Yiping, DENG Xianqin, XU Peng, et al. Analysis of SF6 leakage detection using infrared imaging[J]. Infrared Technology, 2022, 44(2): 198-204. http://hwjs.nvir.cn/article/id/54e422ef-acce-4c36-9dbc-66801c99d8be
[5]	王俊波, 徐鑫, 刘志陆, 等. 电气设备SF6气体泄漏的红外检测技术[J]. 无损检测, 2017, 39(8): 43-46. https://www.cnki.com.cn/Article/CJFDTOTAL-WSJC201708011.htm WANG Junbo, XU Xin, LIU Zhilu. Infrared detection technology of SF6 gas leakage of electrical equipment [J]. Nondestructive Testing, 2017, 39(8): 43-46. https://www.cnki.com.cn/Article/CJFDTOTAL-WSJC201708011.htm
[6]	李军卫, 张英, 赵乐, 等. 基于红外视频图像处理的瓷柱式SF6断路器泄漏区域检测研究[J]. 高压电器, 2018, 54(12): 50-55. https://www.cnki.com.cn/Article/CJFDTOTAL-GYDQ201812009.htm LI Junwei, ZHANG Ying, ZHAO Le, et al. Detection of leakage area of porcelain column SF6 circuit breaker based on infrared video image processing [J]. High Voltage Apparatus, 2018, 54(12): 50-55. https://www.cnki.com.cn/Article/CJFDTOTAL-GYDQ201812009.htm
[7]	刘陈瑶, 胡梦竹, 张龙飞. 基于改进CLAHE的SF6红外图像增强[J]. 光学技术, 2021, 47(1): 107-112. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS202101019.htm LIU Chenyao, HU Mengzhu, ZHANG Longfei. Infrared image enhancement of SF6 based on improved CLAHE[J]. Optical Technique, 2021, 47(1): 107-112. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS202101019.htm
[8]	张志恒. 基于图像自适应分解及多方向特征的多聚焦图像融合算法研究[D]. 西安: 西安电子科技大学, 2021. ZHANG Zhiheng. Research on Multi-focus Images Fusion Algorithm Based on Image Adaptivc Decomposition and Multi-directional Features[D]. Xi'an: XIDIAN University, 2021.
[9]	Land E H, McCann J J. Lightness and retinex theory[J]. J. Opt. Soc. Am. , 1971, 61(1): 1-11.
[10]	Jobson D J, Rahman Z, Woodell G A. Properties and performance of a center/surround retinex[J]. IEEE Transactions on Image Process, 1997, 6(3): 451-462.
[11]	HE Kaiming, SUN Jian, TANG Xiaoou. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409.
[12]	乔闹生. 一种改进的直方图均衡化[J]. 光学技术, 2008, 34(S1): 141-142. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS2008S1047.htm QIAO Naosheng. An improved histogram equalization[J]. Optical Technique, 2008, 34(S1): 141-142. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS2008S1047.htm