Wavelet Denoising Implementation Example

This presents a MATLAB implementation example for wavelet denoising, which performs two-stage denoising operations.

First, import the data and perform necessary preprocessing operations, which may include data normalization or handling missing values. Then, select appropriate wavelet functions (such as 'db4' or 'sym8') and threshold selection methods (like 'rigrsure', 'sqtwolog', or 'heursure'). The implementation involves applying wavelet transform to the noisy data using MATLAB's wavelet toolbox functions. After the first denoising stage, evaluate the denoising effectiveness by calculating metrics like Signal-to-Noise Ratio (SNR) or Mean Squared Error (MSE) to determine if a second denoising operation is required. The second denoising stage may employ different threshold parameters or wavelet levels based on the initial results. Finally, output the denoised data and perform comprehensive result analysis, including visual comparison plots and quantitative performance metrics to validate the denoising quality.