5. Multidimensional wavelet bases
Multidimensional, and in particular two-dimensional, AMRs are important in fields such as image processing, computer vision and turbulence studies. In these applications, the raw data is sometimes too large to be processed in real time by sophisticated algorithms. Wherever possible, the aim is to extract the essential information or details present in the data. Multidimensional wavelets can be used to extract such features. In the following, we will illustrate our point in the 2D case.
5.1 Separable multiresolution analysis
The simplest and most commonly used multidimensional extension of 1D AMR is obtained by considering tensor products of vector spaces
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Multidimensional wavelet bases
Some applications
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