Multiple Denoising Methods Based on Wavelet Transform

Experimental Study: The implementation of multiple denoising methods based on wavelet transform in MATLAB represents a highly significant research topic. Wavelet transform serves as a powerful mathematical tool widely applied in signal and image processing for noise reduction operations. In this experiment, we investigate different denoising approaches and implement them using MATLAB software for comparative analysis. The methods include wavelet threshold denoising, wavelet soft-threshold denoising, and wavelet hard-threshold denoising. Through performance and result comparisons of these techniques, we gain deeper insights into their applications and underlying mechanisms. Key implementation aspects involve using MATLAB's Wavelet Toolbox functions such as wden for automatic denoising, wthresh for threshold applications, and custom scripts for multi-level wavelet decomposition using wavedec and reconstruction via waverec. The experiment provides valuable opportunities to explore wavelet transform theory and practical signal processing applications, including threshold selection strategies (universal threshold, SURE threshold) and level-dependent parameter optimization.