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Wavelets are often used to analyse piece-wise smooth signals. Wavelet coefficients can efficiently represent a signal which has led to data compression algorithms using wavelets. 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) == Discrete wavelet transform is extended to the multidimensional case using the tensor product of well known 1-D wavelets. In 2-D for example, the tensor product space for 2-D is decomposed into four tensor product vector spaces as (HREF="title=Tensor products in a wavelet setting">url=http://www.uio.no/studier/emner/matnat/math/MAT-INF2360/v12/tensorwavelet.pdf )〕 ) } This leads to the concept of multidimensional separable DWT similar in principle to multidimensional DFT. gives the approximation coefficients and other subbands: low-high (LH) subband, high-low (HL) subband, high-high (HH) subband, give detail coefficients 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Wavelet for multidimensional signals analysis」の詳細全文を読む スポンサード リンク
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