MATLAB Source Code for Wavelet Transform-Based Spectral Preprocessing

This is a MATLAB source program designed for spectral preprocessing using wavelet transform. Wavelet transform is a signal processing technique that decomposes signals into sub-signals of different frequencies, enabling more effective analysis of spectral characteristics. Spectral preprocessing refers to the signal processing steps performed prior to spectral analysis, which can remove noise and enhance signal features. The implementation utilizes MATLAB's Wavelet Toolbox functions (such as wavedec, waverec, and wdenoise) to perform multi-level decomposition and reconstruction of spectral data. The algorithm typically involves selecting appropriate wavelet bases (e.g., Daubechies, Symlets) and decomposition levels based on signal characteristics. Key preprocessing steps include threshold-based denoising, baseline correction, and feature enhancement through wavelet coefficient manipulation. This source code provides a framework for wavelet-based spectral preprocessing, establishing improved data quality for subsequent spectral analysis tasks like pattern recognition, quantitative analysis, and classification. The program includes configurable parameters for wavelet selection, thresholding methods, and decomposition levels to accommodate different spectral data types.