Determining Decomposition Levels for Wavelet Denoising Using Sparsity Plot
Determining Decomposition Levels for Wavelet Denoising Using Sparsity Plot
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DOI：
10.1109/access.2021.3103497
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Srivastava, Madhur
中科院分区：
文献类型：
--
作者：
Bekerman, William;Srivastava, Madhur
提出了一种小波去噪中噪声阈值化分解层数的选择方法。必须确定准确的分解水平，以避免噪声阈值化导致的噪声降低不足和/或信号失真。我们引入了稀疏图的概念，它捕捉了从有噪声到无噪声细节分量的突然过渡，很容易揭示最大分解水平的截止值。该方法使用稀疏参数来确定每个细节分量中的噪声存在，并测量稀疏值的幅度变化，以区分有噪声和无噪声的细节分量。该方法进行了测试的模型和实验信号，并证明有效的各种信号长度和类型，以及不同的信噪比（SNR）。该方法可以嵌入任何小波去噪方法，以提高其性能。该代码可通过GitHub和denoising.cornell.edu以及相应作者的小组网站（http：signalsciencelab.com）获得。
We present a method to select decomposition levels for noise thresholding in wavelet denoising. It is essential to determine the accurate decomposition levels to avoid inadequate noise reduction and/or signal distortion by noise thresholding. We introduce the concept of sparsity plot that captures the abrupt transition from noisy to noise-free Detail component, readily revealing the cut-off for the maximum decomposition levels. The method uses the sparsity parameter to determine the noise presence in each detail component and measures the magnitude change in the sparsity values to distinguish between noisy and noise-free Detail components. The method is tested on both model and experimental signals, and proves effective for various signal lengths and types, as well as different Signal-to-Noise Ratios (SNRs). The method can be embedded with any wavelet denoising method to improve its performance. The code is available via GitHub and denoising.cornell.edu, as well as the corresponding author's group website (http://signalsciencelab.com).