Improved threshold denoising method based on wavelet transform

Zhao, Ran; Cui, Huimin

doi:10.1109/icmic.2015.7409352

Cited by 36 publications

(23 citation statements)

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“…We used the two different examples (Figures 5–8 and Figure 9) to show and compare the denoised results. The SUREShrink and Minimax methods are the optimized hybrid scale dependent threshold selection schemes in the current literature [24], [48], whereas other newer methods [9], [37]–[39] use a thresholding function other than hard and soft in an effort to obtain better denoising. Filtering methods are not compared with the new method because standard wavelet denoising methods perform better than them as shown in ESR [30] and in general [24], [25].…”

Section: Esr Experiments and Resultsmentioning

confidence: 99%

“…Note that the results of non-soft and non-hard thresholding function methods [9], [37]–[39] were similar for example 2, and hence, only Zhang and Song’s method [39] is shown. As can be seen, the current state-of-the-art methods are either not very effective at removing baseline noise and/or they distort the signal peak heights.…”

Section: Esr Experiments and Resultsmentioning

confidence: 99%

“…On the other hand, soft thresholding performs better when a Detail wavelet coefficient contains both signal and noise. There are other thresholding functions in the literature that can also be used; for example, see [9], [36]–[39]. …”

Section: Wavelet Shrinkage Denoising Methodsmentioning

confidence: 99%

“…Example 1: Comparison of New Method with Denoising methods [9], [37]–[39] other than soft and hard thresholding at 16 scans.…”

Section: Figmentioning

confidence: 99%

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A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds

2016

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A new method is presented to denoise 1-D experimental signals using wavelet transforms. Although the state-of- the-art wavelet denoising methods perform better than other denoising methods, they are not very effective for experimental signals. Unlike images and other signals, experimental signals in chemical and biophysical applications for example, are less tolerant to signal distortion and under-denoising caused by the standard wavelet denoising methods. The new method 1) provides a method to select the number of decomposition levels to denoise, 2) uses a new formula to calculate noise thresholds that does not require noise estimation, 3) uses separate noise thresholds for positive and negative wavelet coefficients, 4) applies denoising to the Approximation component, and 5) allows the flexibility to adjust the noise thresholds. The new method is applied to continuous wave electron spin resonance (cw-ESR) spectra and it is found that it increases the signal-to-noise ratio (SNR) by more than 32 dB without distorting the signal, whereas standard denoising methods improve the SNR by less than 10 dB and with some distortion. Also, its computation time is more than 6 times faster.

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Section: Esr Experiments and Resultsmentioning

confidence: 99%

Section: Esr Experiments and Resultsmentioning

confidence: 99%

Section: Wavelet Shrinkage Denoising Methodsmentioning

confidence: 99%

“…Example 1: Comparison of New Method with Denoising methods [9], [37]–[39] other than soft and hard thresholding at 16 scans.…”

Section: Figmentioning

confidence: 99%

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A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds

2016

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“…This study uses Mallet algorithm for wavelet decomposition [9]. Assuming that X(n) is heart sound signal mixed with Gauss white noise, S(n) is the original heart sound signal, and e(n) represents the white noise N(0,1), thus X(n)=S(n)+e(n).…”

Section: Mallet Wavelet Algorithmmentioning

confidence: 99%

A Noise Reduction Algorithm of Heart Sound Based on Wavelet Shrinkage

Sun¹,

Yu²,

Miao³

et al. 2017

Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)

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Abstract. In this paper, a kind of noise reduction algorithm is proposed based on wavelet shrinkage technique to acquire heart sound signal while no distortion. Firstly, Haar, Daubechies, Symlets and Coiflets orthogonal wavelets were studied and according to the statistical results, Coif5 wavelet was chosen for the decomposition and reconstruction of heart sound signal. To get the better noise reduction effects, a smooth and continuous adaptive elastic threshold function was designed for wavelet shrinkage, which could perfectly overcome the discontinuous shortage of hard threshold function, especially under Heursure rule when the SNR was less than 50dB. In addition, the results showed that the energy in each layers of Coif5 wavelet differed significantly among different kinds of heart diseases. So the coif5 wavelet may be suitable for feature extraction of heart sound signal and classifications of pathological heart sounds in future research.

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Study on a Denoising Algorithm of Improved Wavelet Threshold

Hou

Ren

2023

Lecture Notes in Electrical Engineering

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Improved threshold denoising method based on wavelet transform

Cited by 36 publications

References 0 publications

A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds

A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds

A Noise Reduction Algorithm of Heart Sound Based on Wavelet Shrinkage

Study on a Denoising Algorithm of Improved Wavelet Threshold

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