Wavelet transform
0Wavelet transform excels as a filtering technique because it allows simultaneous analysis of both time and frequency in a localized manner. By using low-pass and high-pass filter banks through multi-level decomposition, it can separate smooth components from rapid changes, making it highly effective for tasks like noise removal, edge detection, and feature extraction in non-stationary signals. In contrast, the Fourier transform decomposes the entire signal into global sinusoidal components, which is powerful for frequency analysis but lacks time localization. As a result, it struggles with detecting short-term events or localized anomalies. Wavelet-based filtering is particularly advantageous for applications such as image processing and biomedical signals, where precise control over both spatial and temporal resolution is crucial. Compared to Fourier-based filters, wavelets provide more adaptive and targeted filtering performance in complex, time-varying environments.