👉 Continuous Wavelet Transform (CWT) is a mathematical technique that extends the concept of Fourier Transform by analyzing signals at multiple scales and resolutions, providing both time and frequency information. Unlike the Fourier Transform, which decomposes a signal into sinusoidal components of fixed frequencies, CWT uses wavelets—functions that are localized in both time and frequency—to capture transient features and non-stationary behaviors of signals. This makes CWT particularly useful for analyzing complex, real-world data where signals change over time, such as in image processing, biomedical signals, and financial time series. By convolving the signal with scaled and translated versions of a mother wavelet, CWT generates a scalogram that visually represents the signal's energy distribution across different scales and times, offering a more detailed and flexible representation compared to traditional Fourier methods.
