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Wavelet coefficients can be computed by passing the signal to be decomposed though a series of filters. In the case of 1-D, there are two filters at every level-one low pass for approximation and one high pass for the details. In the multidimensional case, the number of filters at each level depends on the number of tensor product vector spaces. For M-D, {{math|2<sup>M</sup>}} filters are necessary at every level. Each of these is called a subband. The subband with all low pass (LLL...) gives the approximation coefficients and all the rest give the detail coefficients at that level.
For example, for {{math|M{{=}}3}} and a signal of size {{math| N1 × N2 × N3}} , a separable DWT can be implemented as follows:<ref name=WavPoly>{{cite web|last1=Cai|first1=Shihua|last2=Li|first2=Keyong|title=Matlab implementation of wavelet transforms|url=http://eeweb.poly.edu/iselesni/WaveletSoftware/index.html}}</ref>
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Applying the 1-D DWT analysis filterbank in dimension {{math|N1}}, it is now split into two chunks of size {{math| {{frac|N1|2}} × N2 × N3}}. Applying 1-D DWT in {{math|N2}} dimension, each of these chunks is split into two more chunks of {{math|{{frac|N1|2}} × {{frac|N2|2}} × N3}}. This repeated in 3-D gives a total of 8 chunks of size {{math| {{frac|N1|2}} × {{frac|N2|2}} × {{frac|N3|2}}}}. The first chunk is passed via a low pass filter in each of these dimensions and the second one via high-pass.
[[File:Filterbank mod try 2.001.png|1024X1000px|framed|The figure shows the 3-D analysis filterbank for 3-D separable DWT]]
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