Abstract
The wavelet transform is extensively utilized in signal denoising due to its benefits of reduced entropy, multiple resolutions, and decorrelation. This paper presents an enhanced wavelet threshold denoising algorithm that combines the existing improved threshold function and threshold selection method, building upon the traditional wavelet threshold denoising algorithm. The enhanced threshold function exhibits improved smoothness and reduced coefficient variation; the novel threshold selection approach integrates the Lipschitz properties of the signal and achieves a higher rate of noise signal elimination. The simulation experiments on denoising demonstrate that the enhanced wavelet threshold denoising algorithm enhances the signal-to-noise ratio (SNR) and mean-square error (MSE) by 14.4% and 58.3% respectively, in comparison to the conventional algorithm. Additionally, it outperforms existing algorithms by 8.4% and 36.5%, showcasing its superior denoising capabilities. These findings validate the performance benefits and practical value of the denoising algorithm proposed in this research paper.
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