基于BEMD和蝙蝠自适应非局部均值去噪的电力设备红外图像增强算法
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1.上海电力大学;2.国网吉林省电力有限公司延边供电公司;3.上海交通大学

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Electrical Equipment Infrared Image Enhancement based on BEMD and Bat adaptive nonlocal mean denoising
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1.Shanghai University of Electric Power;2.Guangan Power Supply Co.,State Yanbian Electric Power Co.Ltd.;3.Shanghai Jiao Tong University

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    摘要:

    针对电力设备红外图像细节模糊,对比度不高的问题,文中设计了基于二维模态分解(BEMD)和蝙蝠自适应非局部均值去噪的算法。首先对原始电力设备红外图像进行BEMD变换,变换后可得到高频分量和低频分量。然后为抑制高频分量中的噪声,文章结合蝙蝠优化算法和非局部均值去噪,有效抑制噪声的同时增强了边缘和细节特征。接着在低频分量采用线性增强算法,扩大电力设备目标与背景环境之间的对比度。最后通过逆BEMD重构得到增强后的红外图像。经比较及应用,文中算法能够有效凸显变电站电力设备红外图像中热区域,方便更早的发现故障,以及对热故障进行定位。

    Abstract:

    In order to solve the problems of fuzzy detail and low contrast of infrared image of electrical equipment, an infrared image processing method based on BEMD(Bidimensional empirical mode decomposion algorithm,BEMD) and bat adaptive nonlocal mean denoising is proposed. Firstly, the original infrared image is decomposed by BEMD. The image can be divided into high-frequency component (bimf) and low-frequency component (residual component). Bat adaptive nonlocal mean denoising is adopted for high frequency component, and only the details and edges of the image are preserved. The low frequency component contains the information and details of the original image, which is linearly enhanced. Finally, the components are reconstructed by inverse BEMD. After comparison and application, this algorithm can effectively highlight the hot area in the infrared image of power equipment, which is convenient for earlier fault detection and fault location.

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历史
  • 收稿日期:2020-12-28
  • 最后修改日期:2021-07-02
  • 录用日期:2021-09-11
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