multiresolutionanalyysin
Multiresolution analysis (MRA) is a mathematical framework used in signal processing, image analysis, and functional analysis to study functions or signals at different scales or resolutions. Introduced by Stephane Mallat in the 1980s, it provides a hierarchical decomposition of data into components representing various levels of detail, enabling efficient analysis and reconstruction.
At its core, MRA involves representing a function or signal as a sum of approximations at multiple
A key component of MRA is the use of multiscale bases, such as wavelet bases, which decompose
In practice, MRA is often implemented using algorithms like the discrete wavelet transform (DWT) or multiresolution
Beyond signal processing, MRA has applications in computer vision, data compression, and machine learning, where hierarchical