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We have already seen in our discussion of The Haar Transform how the 1-D Haar transform (or wavelet) could be extended to 2-D by filtering therows and columns of an image separably.
All 1-D 2-band wavelet filter banks can be extended in a similar way. shows two levels of a 2-D filter tree. The input image at each level is split into 4bands (Lo-Lo = , Lo-Hi = , Hi-Lo = , and Hi-Hi = ) using the lowpass and highpass wavelet filters on the rows and columns in turn. The Lo-Lo band subimage is then used as the input image to the next level. Typically 4 levels are used, as for the Haar transform.
Filtering of the rows of an image by and of the columns by , where , = 0 or 1, is equivalent to filtering by the 2-D filter:
To obtain the impulse responses of the four 2-D filters at each level of the 2-D DWT we form from and using with = 00, 01, 10 and 11.
shows the impulse responses at level 4 as images for three 2-D wavelet filtersets, formed from the following 1-D wavelet filter sets:
The 2-D frequency responses of the level 1 filters, derived from the LeGall 3,5-tap filters, are shown in figs (in mesh form) and (in contour form). These are obtained by substituting and into . demonstrates that the 2-D frequency response is just the product of the responses of the relevant1-D filters.
and are the equivalent plots for the 2-D filters derived from the near-balanced 13,19-tap filters. Wesee the much sharper cut-offs and better defined pass and stop bands of these filters. The high-band filters no longer exhibitgain peaks, which are rather undesirable features of the LeGall 5-tap filters.
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