Abstract
The concept of multiresolution is intuitively related to the study of signals or images at different levels of resolution—almost like a pyramid. The resolution of a signal is a qualitative description associated with its frequency content. For a low-pass signal, the lower its frequency content (the narrower the bandwidth), the coarser is its resolution. In signal processing, a low-pass and subsampled version of a signal is usually a good coarse approximation for many real world signals. Multiresolution is especially evident in image processing and computer vision, where coarse versions of an image are often used as a first approximation in computational algorithms.
Multiresolution analysis provides a natural framework for the understanding of wavelet bases, and for the construction of new examples. The history of the formulation of multiresolution analysis is a beautiful example of applications stimulating theoretical development. Ingrid Daubechies
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Debnath, L., Shah, F.A. (2015). Multiresolution Analysis and Construction of Wavelets. In: Wavelet Transforms and Their Applications. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-0-8176-8418-1_7
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DOI: https://doi.org/10.1007/978-0-8176-8418-1_7
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