Wavelet Entropy Adaptive Denoising with Layer Entropy Values (Absolute Value Representation) and Weight Calculation

Wavelet entropy adaptive denoising with calculation of layer entropy values (represented as absolute values) and their corresponding weights constitutes one of the key research focuses in my original paper. In this study, I implement wavelet entropy adaptive denoising techniques to process signal data, effectively reducing noise interference through multi-level wavelet decomposition and entropy-based thresholding algorithms. The methodology involves computing entropy values for each decomposition layer using absolute value representations to maintain consistent scaling across frequency bands. Additionally, I determine weighting factors for different layers to better understand their individual contributions to the overall denoising performance. This research employs MATLAB's wavelet toolbox functions for decomposition and custom entropy calculation algorithms for adaptive threshold selection. The implementation includes wavelet packet decomposition for detailed frequency analysis, entropy calculation using Shannon's formula, and layer weighting based on energy distribution patterns. These research findings provide valuable insights into the applications and advantages of wavelet entropy adaptive denoising in signal processing applications.