A gradient-projective basis of compactly supported wavelets in dimension n > 1
Open Mathematics (2013)
- Volume: 11, Issue: 7, page 1317-1333
- ISSN: 2391-5455
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topGuy Battle. "A gradient-projective basis of compactly supported wavelets in dimension n > 1." Open Mathematics 11.7 (2013): 1317-1333. <http://eudml.org/doc/269050>.
@article{GuyBattle2013,
abstract = {A given set W = W X of n-variable class C 1 functions is a gradient-projective basis if for every tempered distribution f whose gradient is square-integrable, the sum $\sum \limits _\chi \{(\int _\{\mathbb \{R\}^n \} \{\nabla f \cdot \} \nabla W_\chi ^* )\} W_\chi $ converges to f with respect to the norm $\left\Vert \{\nabla ( \cdot )\} \right\Vert _\{L^2 (\mathbb \{R\}^n )\} $ . The set is not necessarily an orthonormal set; the orthonormal expansion formula is just an element of the convex set of valid expansions of the given function f over W. We construct a gradient-projective basis W = W x of compactly supported class C 2−ɛ functions on ℝn such that [...] where X has the structure $\chi = (\tilde\{\chi \},\nu )$ , ν ∈ ℤ. W is a wavelet set in the sense that the functions indexed by $\tilde\{\chi \}$ are generated by an averaging of lattice translations with wave propagations, and there are two additional discrete parameters associated with the latter. These functions indexed by $\tilde\{\chi \}$ are the unit-scale wavelets. The support volumes of our unit-scale wavelets are not uniformly bounded, however. While the practical value of this construction is doubtful, our motivation is theoretical. The point is that a gradient-orthonormal basis of compactly supported wavelets has never been constructed in dimension n > 1. (In one dimension the construction of such a basis is easy - just anti-differentiate the Haar functions.)},
author = {Guy Battle},
journal = {Open Mathematics},
keywords = {Fourier transform; Wavelets; wavelets},
language = {eng},
number = {7},
pages = {1317-1333},
title = {A gradient-projective basis of compactly supported wavelets in dimension n > 1},
url = {http://eudml.org/doc/269050},
volume = {11},
year = {2013},
}
TY - JOUR
AU - Guy Battle
TI - A gradient-projective basis of compactly supported wavelets in dimension n > 1
JO - Open Mathematics
PY - 2013
VL - 11
IS - 7
SP - 1317
EP - 1333
AB - A given set W = W X of n-variable class C 1 functions is a gradient-projective basis if for every tempered distribution f whose gradient is square-integrable, the sum $\sum \limits _\chi {(\int _{\mathbb {R}^n } {\nabla f \cdot } \nabla W_\chi ^* )} W_\chi $ converges to f with respect to the norm $\left\Vert {\nabla ( \cdot )} \right\Vert _{L^2 (\mathbb {R}^n )} $ . The set is not necessarily an orthonormal set; the orthonormal expansion formula is just an element of the convex set of valid expansions of the given function f over W. We construct a gradient-projective basis W = W x of compactly supported class C 2−ɛ functions on ℝn such that [...] where X has the structure $\chi = (\tilde{\chi },\nu )$ , ν ∈ ℤ. W is a wavelet set in the sense that the functions indexed by $\tilde{\chi }$ are generated by an averaging of lattice translations with wave propagations, and there are two additional discrete parameters associated with the latter. These functions indexed by $\tilde{\chi }$ are the unit-scale wavelets. The support volumes of our unit-scale wavelets are not uniformly bounded, however. While the practical value of this construction is doubtful, our motivation is theoretical. The point is that a gradient-orthonormal basis of compactly supported wavelets has never been constructed in dimension n > 1. (In one dimension the construction of such a basis is easy - just anti-differentiate the Haar functions.)
LA - eng
KW - Fourier transform; Wavelets; wavelets
UR - http://eudml.org/doc/269050
ER -
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