Applications of NSST enhancement based on adaptive longicorn optimized segmentation and improved modulus in infrared image of electrical equipment
Xin Zhang, Qiang Yao, Xiaohua Zhang, Zhenwei Xie, chunping Liu, Xin Wang · International Conference on Mechanical Engineering, Measurement Control, and Instrumentation · 2021
The normal state of power equipment is directly related to the operation of the system. At present, the most widely used method is to use infrared image to implement real-time monitoring of the operation status of power equipment. In order to solve the problems of noise, blur and low contrast in infrared detection, an infrared image NSST enhancement algorithm based on adaptive segmentation and improved fuzzy enhancement is designed in this paper. The original infrared image is transformed into high-frequency and low-frequency components in NSST domain. Then, the high-frequency component with noise is denoised by the spatial adaptive noise smoothing algorithm, and the improved fuzzy enhancement is used.The low-frequency component with the main body of power equipment is segmented into background and foreground parts by adaptive Longhorn, and then enhanced separately. Finally, the enhanced high-frequency and low-frequency components are inversely transformed by NSST to form the final enhanced image. Compared with the other three traditional algorithms, this algorithm has the advantages: it can not only filter the infrared image noise of power equipment effectively, but also improve the infrared image contrast, making the infrared image conform to the human visual effect, and it is easier for the human eye to recognize the fault. It is very helpful to detect and locate the thermal fault of power equipment.