Wavelet transforms are a mathematical means for performing signal analysis when signal frequency varies over time. For certain classes of signals and images, wavelet analysis provides more precise information about signal data than other signal analysis techniques.
One can perform wavelet analysis in MATLAB and Wavelet Toolbox, which lets one compute wavelet transform coefficients. The toolbox includes many wavelet transforms that use wavelet frame representations, such as continuous, discrete, nondecimated, and stationary wavelet transforms. These products can be used for image compression, feature extraction, signal denoising, data compression, and time-series analysis.
Using continuous wavelet analysis, you can explore how spectral features evolve over time, identify common time-varying patterns in two signals, and perform time-localized filtering. Using discrete wavelet analysis, you can analyze signals and images at different resolutions to detect changepoints, discontinuities, and other events not readily visible in raw data. You can compare signal statistics on multiple scales, and perform fractal analysis of data to reveal hidden patterns. Once the MATLAB license has been purchased I will begin using this toolkit. In the meantime I am looking at other toolkits in MATLAB and using Python.