A New Approach for Optimal Decomposition Level Selection in Wavelet De-Noising

Zhang, Yong Xin; Chen, Li; Jia, Jian; Fang, Dingyi

doi:10.4028/www.scientific.net/amm.333-335.540

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…In the wavelet transform, the functions are crucial and should be selected in a comprehensive manner in terms of support length, symmetry, regularity and similarity. The functions, such as db, sym, coif and fk [11,[17][18][19][20][21][22][23], are the most commonly used. db is short for Daubechies.…”

Section: Wavelet Transformmentioning

confidence: 99%

Research on Image Denoising in Edge Detection Based on Wavelet Transform

2023

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Photographing images is used as a common detection tool during the process of bridge maintenance. The edges in an image can provide a lot of valuable information, but the detection and extraction of edge details are often affected by the image noise. This study proposes an algorithm for wavelet transform to denoise the image before edge detection, which can improve the signal-to-noise ratio of the image and retain as much edge information as possible. In this study, four wavelet functions and four decomposition levels are used to decompose the image, filter the coefficients and reconstruct the image. The PSNR and MSE of the denoised images were compared, and the results showed that the sym5 wavelet function with three-level decomposition has the best overall denoising performance, in which the PSNR and MSE of the denoised images were 23.48 dB and 299.49, respectively. In this study, the canny algorithm was used to detect the edges of the images, and the detection results visually demonstrate the difference between before and after denoising. In order to further evaluate the denoising performance, this study also performed edge detection on images processed by both wavelet transform and the current widely used Gaussian filter, and it calculated the Pratt quality factor of the edge detection results, which were 0.53 and 0.47, respectively. This indicates that the use of wavelet transform to remove noise is more beneficial to the improvement of the subsequent edge detection results.

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Section: Wavelet Transformmentioning

confidence: 99%

Research on Image Denoising in Edge Detection Based on Wavelet Transform

2023

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“…In the wavelet transform, the use of wavelet basis functions is very critical and needed to comprehensive considerate the support length, symmetry, regularity and similarity and so on. The most popular ones are db, sym and coif [18][19][20][21][22]. The choice of the number of decomposition layers is also crucial.…”

Section: Figure 1 Transform Denoising General Processmentioning

confidence: 99%

“…Hard thresholding has advantages in the sense of mean squared deviation. Soft thresholding functions, however, may produce jump points, while soft thresholding functions have better continuity but produce biases that affect the reconstructed signal [5,[23][24][25][26] . In this paper a hard thresholding function is chosen to process the coefficients, followed by wavelet reconstruction.…”

Section: Image Denoisingmentioning

confidence: 99%

Research on Image Denoising in edge detection Based on Wavelet Transform

Ning¹,

Han²,

Zhu³

et al. 2022

Preprint

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In the process of image feature extraction, noise in the image will greatly affect the accuracy of edge detection. In this paper, the image is filtered to remove noise before edge detection by using the algorithm of wavelet transformation. Different wavelet functions are used to decompose image. Based on the experimental results, the best denoising wavelet function is selected. Canny algorithm is used to detect the edge of the denoised image, and the result of edge detection is evaluated according to the Pratt quality factor. It is proved that wavelet transform can improve the edge detection results.

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“…To address these issues, researchers have implemented time-frequency analysis methods like the short-time Fourier transform (STFT) (Gaci, 2016) and wavelet-based techniques (Doroslovacki and Fan, 1993; Strang, 1994), but these methods suffer from their own share of problems. For example, when applying wavelet-based methods, the wavelet basis (Donoho and Johnstone, 1994) must be optimized for the signal, and this, as well as the decomposition level, may hinder the success of the denoising approach (Zhang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Component-scaled signal reconstruction for enhanced noise filtration

Singh

Moore

2022

Journal of Vibration and Control

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This research introduces a procedure for signal denoising based on linear combinations of intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). The method, termed component-scaled signal reconstruction, employs the standard EMD algorithm, with no enhancements to decompose the signal into a set of IMFs. The problem of mode mixing is leveraged for noise removal by constructing an optimal linear combination of the potentially mixed IMFs. The optimal linear combination is determined using an optimization routine with an objective function that maximizes and minimizes the information and noise, respectively, in the denoised signal. The method is demonstrated by applying it to a computer-generated voice sample and the displacement response of a cantilever beam with local stiffness nonlinearity. In the first application, the noise is introduced into the sample manually by adding a Gaussian white-noise signal to the signal. In the second application, the response of the entire beam is filmed using two 1-megapixel cameras, and the three-dimensional displacement field is extracted using digital image correlation. The noise in this application arises entirely from the images captured. The proposed method is compared to existing EMD, ensemble EMD, and LMD based denoising approaches and is found to perform better.

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A New Approach for Optimal Decomposition Level Selection in Wavelet De-Noising

Cited by 3 publications

References 13 publications

Research on Image Denoising in Edge Detection Based on Wavelet Transform

Research on Image Denoising in Edge Detection Based on Wavelet Transform

Research on Image Denoising in edge detection Based on Wavelet Transform

Component-scaled signal reconstruction for enhanced noise filtration

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