A Novel and Optimized Sine–Cosine Transform Wavelet Threshold Denoising Method Based on the sym4 Basis Function and Adaptive Threshold Related to Noise Intensity

Abstract

In digital shearography, the speckle noise of the phase fringe pattern has a negative effect on the accuracy and reliability of the phase unwrapping procedure. A novel and optimized sine–cosine transform wavelet threshold denoising method is proposed to suppress speckle noise. Fast phase denoising can be achieved by using the proposed method while preserving the phase reversal information. The details of the selected wavelet basis function, the optimal decomposition level, the threshold function, and the denoising threshold are also provided in this manuscript. In particular, the decomposition level is analyzed and optimized through simulation analysis according to the speckle suppression index and the adaptive denoising method. The experimental results show that the proposed method has more adaptive ability in practical application than the sine–cosine transform average denoising method with the selected mask and iterative procedure, which speeds the denoising process up and takes better-unwrapped phase patterns.

Multiscale Bayes Adaptive Threshold Wavelet Transform Geomagnetic Basemap Denoising Taking Residual Constraints into Account

“…where l(x, y) denotes the brightness, c(x, y) denotes the contrast, s(x, y) denotes the structure, x denotes the original image, and y denotes the noisy image. The accuracy of different denoising algorithms can be reflected by calculating the root mean square error between the denoised image and the original image and examining the effect of denoising algorithms on the accuracy of geomagnetic reference maps [16].…”

“…The accuracy of different denoising algorithms can be reflected by calculating the root mean square error between the denoised image and the original image and examining the effect of denoising algorithms on the accuracy of geomagnetic reference maps [ 16 ]. where represents the value of the original basemap and represents the value of the processed basemap.…”

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

“…This paper proposes an improved adversarial transfer network (IATN) for bearing fault diagnosis under variable working conditions, inspired by the effectiveness of distancebased and adversarial-based domain adaptation. Specifically, this paper combines an adversarial transfer network with a short-time Fourier transform (STFT) [41] to obtain satisfactory results with lighter networks. Then, this paper employs an attention module to enhance feature fusion.…”