Conventional Wavelet Threshold Denoising Methods
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Resource Overview
Conventional wavelet threshold denoising methods operate under the assumption that wavelet coefficients are independent, neglecting their correlations across adjacent scales, which results in an inherent trade-off between noise removal and preservation of useful high-frequency information.
Detailed Documentation
In traditional wavelet threshold denoising methods, wavelet coefficients are assumed to be mutually independent, without considering their correlations across adjacent scales. This limitation often creates a dilemma between preserving valuable high-frequency information and achieving effective noise reduction. To address this issue, recent advancements have introduced novel techniques that incorporate the correlations among wavelet coefficients, thereby achieving a better balance between denoising performance and high-frequency signal retention. From an implementation perspective, these improved methods typically involve modifying threshold selection algorithms (such as Stein's Unbiased Risk Estimate - SURE) or employing scale-adaptive thresholding schemes that account for inter-scale dependencies through correlation models or Bayesian frameworks.
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