Haar Wavelet Transform

The Haar Wavelet Transform is a signal processing method based on wavelet theory that decomposes signals into multiple sub-signals at different frequency levels. Through inverse transformation of these sub-signals, the original signal can be reconstructed. In MATLAB, we can implement this method using Haar wavelet transform source code that typically involves key functions like wavedec for decomposition and waverec for reconstruction. The implementation follows a multi-resolution analysis approach where signals are recursively divided into approximation and detail coefficients using Haar scaling and wavelet functions. By utilizing this source code, users can perform Haar wavelet transformation on signals, obtain decomposed sub-signals, and analyze frequency characteristics through methods like threshold-based denoising (using wthresh) or energy distribution analysis. The code structure generally includes steps for signal padding, wavelet decomposition level selection, coefficient processing, and signal reconstruction validation.