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Line 1: {{Orphan\|date=November 2015}} [[Wavelet]]s are often used to analyse piece-wise smooth signals.<ref>{{cite book\|last1=Mallat\|first1=Stéphane\|title=A Wavelet Tour of Signal Processing\|date=2008\|publisher=Academic Press}}</ref> Wavelet coefficients can efficiently represent a signal which has led to data compression algorithms using wavelets.<ref>{{cite journal\|last1=Devore\|first1=Ronald\|last2=Jawerth\|first2=Bjorn\|last3=Lucier\|first3=Bradley\|title=Data compression using wavelets: error, smoothness and quantization\|journal=IEEE Data Compression Conference~~,IEEE~~\|date=8 April 1991\|pages=186–195\|doi=10.1109/DCC.1991.213386}}</ref> Wavelet analysis is extended for [[multidimensional signal processing]] as well. This article introduces a few methods for wavelet synthesis and analysis for multidimensional signals. There also occur challenges such as directivity in multidimensional case. == Multidimensional separable Discrete Wavelet Transform (DWT) == Line 92: [http://www.uio.no/studier/emner/matnat/math/MAT-INF2360/v12/tensorwavelet.pdf Tensor products in wavelet settings] [http://eeweb.poly.edu/iselesni/WaveletSoftware/index.html Matlab implementation of wavelet transforms] *[https://arxiv.org/abs/1101.5320 A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity], a review on 2D (two-dimensional) wavelet representations [[Category:Multidimensional signal processing]]

Wavelet for multidimensional signals analysis: Difference between revisions