Wavelet Denoising Program

The wavelet denoising program source code represents a signal processing algorithm that effectively reduces noise while preserving important signal features. This implementation typically includes three core components: wavelet decomposition, threshold processing, and signal reconstruction. Wavelet decomposition algorithms (such as those implemented using MATLAB's wavedec function) break down signals into different frequency subbands through multi-level decomposition. This process separates noise components from the actual signal content across various resolution levels. Threshold setting constitutes the critical denoising phase, where hard or soft thresholding techniques (implemented via wthresh function) determine which wavelet coefficients to retain or discard. The proper threshold selection (using methods like universal threshold or minimax threshold) effectively eliminates noise-dominated coefficients while preserving signal-related components. Signal reconstruction (achieved through waverec function) then synthesizes the thresholded wavelet coefficients back into the clean signal. This denoising approach significantly enhances signal quality by removing high-frequency noise components while maintaining important signal characteristics through proper coefficient thresholding at different decomposition levels.