ArticleIEEE access : practical innovations, open solutions2024
Optimal Wavelet Selection for Signal Denoising.
Article in IEEE access : practical innovations, open solutions, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- WAND: Wavelet Analysis-Based Neural Decomposition of MRS Signals for Artifact Removal.NMR in biomedicine · 2025Article
- ICEEMDAN-RPE-AITD algorithm for magnetic field signals of magnetic targets.Scientific reports · 2025Article
- Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation.Current research in food science · 2025Article
- Noise reduction in brain magnetic resonance imaging using adaptive wavelet thresholding based on linear prediction factor.Frontiers in neuroscience · 2024Article
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Authors and funding
3 authors.
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Abstract
Wavelet denoising plays a key role in removing noise from signals and is widely used in many applications. In denoising, selection of the mother wavelet is desirable for maximizing the separation of noise and signal coefficients in the wavelet domain for effective noise thresholding. At present, wavelet selection is carried out in a heuristic manner or using a trial-and-error that is time consuming and prone to error, including human bias. This paper introduces a universal method to select optimal wavelets based on the sparsity of Detail components in the wavelet domain, an empirical approach. A mean of sparsity change (
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Registered trials
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