Infrared image of electrical equipment enhancement using grey wolf adaptive nonlocal mean denoising and adaptive fuzzy enhancement

Xin Zhang; Xikui Sheng; Chunsheng Li; Jiaxiang Zhu; Shanshan Li; Xin Wang

doi:10.1117/12.2611682

14 October 2021 Infrared image of electrical equipment enhancement using grey wolf adaptive nonlocal mean denoising and adaptive fuzzy enhancement

Xin Zhang, Xikui Sheng, Chunsheng Li, Jiaxiang Zhu, Shanshan Li, Xin Wang

Author Affiliations +

Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 119300Q (2021) https://doi.org/10.1117/12.2611682
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China

Abstract

In this paper, the infrared image of power equipment contains noise, blur and so on, which makes it impossible to accurately judge and locate the infrared fault. An infrared image enhancement algorithm based on grey wolf adaptive non local mean denoising and adaptive fuzzy enhancement is proposed. Firstly, grey wolf adaptive non local mean denoising is used to denoise the initial infrared image, and then NSCT transform is performed. After the transform, grey wolf algorithm is used to optimize the fuzzy parameters of high-frequency components, and then enhancement is performed; the low frequency component is linearly enhanced. Then the NSCT inverse transform is performed. After the algorithm verification, it is shown that the algorithm is effective in infrared image denoising and enhancement, and the evaluation index also verifies the effectiveness of the algorithm.

Citation Download Citation

Xin Zhang, Xikui Sheng, Chunsheng Li, Jiaxiang Zhu, Shanshan Li, and Xin Wang "Infrared image of electrical equipment enhancement using grey wolf adaptive nonlocal mean denoising and adaptive fuzzy enhancement", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 119300Q (14 October 2021); https://doi.org/10.1117/12.2611682

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