Denoising of Laser Self-Mixing Interference by Improved Wavelet Threshold for High Performance of Displacement Reconstruction

Liu, Hui; You, Yaqiang; Li, Sijia; He, Dan; Sun, Jian; Wang, Jingwei; Hou, Dong

doi:10.3390/photonics10080943

Cited by 4 publications

(3 citation statements)

References 30 publications

(37 reference statements)

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“…Among them, noise reduction is the most important step in experimental pre-processing. In this work, we used the wavelet thresholding method ( 37 ) to denoise the raw data, in order to remove the effects of high-frequency noise, artifacts, electromyographic noise, and respiratory movements on ECG. First, the db6 wavelet was used to decompose the signal into levels 1–3, and subsequently, decomposed signals were adjusted to the baseline using the rigrsure soft threshold selection method.…”

Section: Methodsmentioning

confidence: 99%

A novel method of swin transformer with time-frequency characteristics for ECG-based arrhythmia detection

Chen,

Wang,

Zhang

et al. 2024

Front. Cardiovasc. Med.

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IntroductionArrhythmia is an important indication of underlying cardiovascular diseases (CVD) and is prevalent worldwide. Accurate diagnosis of arrhythmia is crucial for timely and effective treatment. Electrocardiogram (ECG) plays a key role in the diagnosis of arrhythmia. With the continuous development of deep learning and machine learning processes in the clinical field, ECG processing algorithms have significantly advanced the field with timely and accurate diagnosis of arrhythmia.MethodsIn this study, we combined the wavelet time-frequency maps with the novel Swin Transformer deep learning model for the automatic detection of cardiac arrhythmias. In specific practice, we used the MIT-BIH arrhythmia dataset, and to improve the signal quality, we removed the high-frequency noise, artifacts, electromyographic noise and respiratory motion effects in the ECG signals by the wavelet thresholding method; we used the complex Morlet wavelet for the feature extraction, and plotted wavelet time-frequency maps to visualise the time-frequency information of the ECG; we introduced the Swin Transformer model for classification and achieve high classification accuracy of ECG signals through hierarchical construction and self attention mechanism, and combines windowed multi-head self-attention (W-MSA) and shifted window-based multi-head self-attention (SW-MSA) to comprehensively utilise the local and global information.ResultsTo enhance the confidence of the experimental results, we evaluated the performance using intra-patient and inter-patient paradigm analyses, and the model classification accuracies reached 99.34% and 98.37%, respectively, which are better than the currently available detection methods.DiscussionThe results reveal that our proposed method is superior to currently available methods for detecting arrhythmia ECG. This provides a new idea for ECG based arrhythmia diagnosis.

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Section: Methodsmentioning

confidence: 99%

A novel method of swin transformer with time-frequency characteristics for ECG-based arrhythmia detection

Chen,

Wang,

Zhang

et al. 2024

Front. Cardiovasc. Med.

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“…To effectively remove noise from underwater acoustic signals, this paper incorporates a correlation function into the CEEMDAN algorithm to determine the optimal decomposition layer, denoted as N. The signal is then decomposed into multiple IMFs ranging from high to low frequencies. [8]- [12] A wavelet threshold is set to filter out noise in the high-frequency IMFs that contain more noise. The result is a reconstructed underwater acoustic signal.…”

Section: Introductionmentioning

confidence: 99%

Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold

Jiang,

Zhang,

et al. 2024

J. Phys.: Conf. Ser.

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Due to the complex noise in the ocean environment, the signal-to-noise ratio of the hydrophone receiving signal is often low, making subsequent signal processing difficult. To solve this problem, this paper proposes using CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) decomposition algorithm combined with an improved wavelet threshold algorithm to process the signal, and obtain the reconstructed signal after denoising. In this method, the noise-containing signal is transformed by the function and decomposed into multiple natural mode components with frequencies ranging from high to low using the CEEMDAN algorithm. The correlation component and the non-correlation component are then determined using the cross-correlation function. The non-correlated compinents are denoised using the improved wavelet threshold method and the denoised signal is obtained by reconstructing the signal. Experimental results show that this method can improve the performance of underwater acoustic signal denoising.

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“…The wavelet thresholds can be divided into hard thresholds and soft thresholds and, generally, a soft threshold has a better denoising performance than a hard threshold [20][21][22][23]. Except for classic image denoising, wavelet-threshold techniques are introduced to denoise laser self-mixing interference signals [24].…”

Section: Introductionmentioning

confidence: 99%

Wavelet and Earth Mover’s Distance Coupling Denoising Techniques

Zhang,

Xu,

Crabbe

2023

Electronics

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The widely used wavelet-thresholding techniques (DWT-H and DWT-S) have a near-optimal behavior that cannot be enhanced by any local denoising filter, but they cannot utilize the similarity of small-size image patches to enhance the denoising performance. Two of the latest improvements (WNLM and NLMW) introduced the Euclidean distance to measure the similarity of image patches, and then used the non-local meaning of similar patches for further denoising. Since the Euclidean distance is not a good similarity measurement, these two improvements are limited. In this study, we introduced the earth mover’s distance (EMD) as the similarity measure of small-scale patches within the wavelet sub-bands of noisy images. Moreover, at higher noise levels, we further incorporated joint bilateral filtering, which can filter both the spatial domain and the intensity domain of images. Denoising simulation experiments on BSDS500 demonstrated that our algorithm outperformed the DWT-H, DWT-S, WNLM, and NLMW algorithms by 4.197 dB, 3.326 dB, 2.097 dB, and 1.162 dB in terms of the average PSNR, and by 0.230, 0.213, 0.132, and 0.085 in terms of the average SSIM.

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Denoising of Laser Self-Mixing Interference by Improved Wavelet Threshold for High Performance of Displacement Reconstruction

Cited by 4 publications

References 30 publications

A novel method of swin transformer with time-frequency characteristics for ECG-based arrhythmia detection

A novel method of swin transformer with time-frequency characteristics for ECG-based arrhythmia detection

Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold

Wavelet and Earth Mover’s Distance Coupling Denoising Techniques

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