A Fiber Optic Ultrasonic Sensor Using Polarization-maintaining Fiber for Partial Discharge Monitoring

Background: The infrared image of electrical equipment often contains snow or even blurred, which makes it difficult to detect and analyze its state. Methods: A prior infrared image denoising and restoring method based on l2-relaxation l0 analysis is proposed. Through the image prior estimation, the problem of image de-blurring and denoising is transformed into the problem of solving the maximum entropy of a posteriori probability, and then the parameters are jointly optimized to widely degrade the image, so that the image is locally sparse from the strip and edge to the linear predictable texture, and the target object to be extracted is obtained by using the alternative iterative solution, to achieve the purpose of denoising and restoring of the original fuzzy infrared image with noise. Two kinds of infrared images with different brightness levels in a 220kV booster station are used to do experiments. Results: Compared with BM3D, TwIST, TVL1C, TVL2C, the experimental results show that the denoising and restoration effect of the proposed method is clearly better than the four methods. The PSNR, ISNR, and SSIM of the proposed method are greater than the others, and the calculation time is the shortest. Conclusion: This method can not only enhance the sparsity of the infrared image target and improve the estimation accuracy, but also has the advantages of minimum image distortion, fast convergence speed, and preserving the target detail edge. This method can provide a new idea for other types of infrared image denoising and restoration.

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A Fiber Optic Ultrasonic Sensor Using Polarization-maintaining Fiber for Partial Discharge Monitoring

Cited by 2 publications

References 8 publications

Different Electrodes Partial Discharge Detections via Fluorescent Fiber

Different Electrodes Partial Discharge Detections via Fluorescent Fiber

Denoising and Restoring of Infrared Image of Power Equipment Based on l2-relaxed l0 Sparse Analysis Priors

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