Revision as of 21:59, 12 November 2019 edit Headbomb (talk \| contribs) Edit filter managers, Autopatrolled, Extended confirmed users, Page movers, File movers, New page reviewers, Pending changes reviewers, Rollbackers, Template editors 473,160 edits Alter: template type. Add: isbn, year, pages, volume, title, chapter, doi, author pars. 1-3. Removed URL that duplicated unique identifier. Converted bare reference to cite template. \| You can use this tool yourself. Report bugs here. \| via #UCB_Gadget ← Previous edit		Revision as of 09:19, 7 December 2019 edit undo Citation bot (talk \| contribs) Bots 5,866,019 edits m Add: hdl. \| You can use this bot yourself. Report bugs here.\| Activated by User:Nemo bis \| via #UCB_webform Next edit →
Line 67: ==Hypercomplex Wavelet Transform== The dual tree '''Hypercomplex Wavelet Transform (HWT)''' developed in <ref name=DHWT>{{Cite book \|doi = 10.1109/ICASSP.2004.1326715\|chapter = Directional hypercomplex wavelets for multidimensional signal analysis and processing\|title = 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing\|volume = 3\|pages = ~~iii-996-9~~iii–996–9\|year = 2004\|last1 = Wai Lam Chan\|last2 = Hyeokho Choi\|last3 = Baraniuk\|first3 = R.G.\|isbn = 0-7803-8484-9\|hdl = 1911/19796}}</ref> consists of a standard DWT tensor and {{math\|2<sup>m -1</sup>}} wavelets obtained from combining the 1-D Hilbert transform of these wavelets along the n-coordinates. In particular a 2-D HWT consists of the standard 2-D separable DWT tensor and three additional components: {{math\| H<sub>x</sub> {ψ(x)<sub>h</sub>ψ(y)<sub>h</sub>} {{=}} ψ(x)<sub>g</sub>ψ(y)<sub>h</sub> }}

Wavelet for multidimensional signals analysis: Difference between revisions