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.
Aiming at the problems of fuzzy, noisy, and low contrast of infrared images in detection of power equipment. This paper designs an improved Otsu segmentation threshold based on seagull optimization and BEEPS filter algorithm in NSST domain. First, the original infrared image is decomposed by NSST to high and low frequency components. Low-frequency component is divided into two parts, the foreground and background, and the enhancement processing is performed separately to each part. BEEPS algorithm is used in this paper for high-frequency component processing. Finally, the processed low-frequency components and high-frequency components are subjected to NSST inverse transformation to obtain the final enhanced image. The algorithm in this paper is compared with the other three algorithms to verify its superiority: it has improved the accuracy of infrared image threshold segmentation and strengthened the depth of field, and increase the brightness of power equipment. The overall contrast of the image is enhanced and the noise part is also effectively filtered out, improving the overall visual effect of the image which is conducive to use thermal effects to determine the operating status of power equipment and the detection and fault location of thermal fault detection.
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.
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