Wavelet transforms are a mathematical means to perform 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. Common applications of wavelet transforms include speech and audio processing, image and video processing, biomedical imaging, and 1-D and 2-D applications in communications and geophysics.
You can perform wavelet analysis in MATLAB® and Wavelet Toolbox™, which lets you compute wavelet transform coefficients. The toolbox includes many wavelet transforms that use wavelet frame representations, such as continuous, discrete, nondecimated, and stationary wavelet transforms. You can use the products for image compression, feature extraction, signal denoising, data compression, and time-series analysis.
Continuous and Discrete Wavelet Analysis (Example)