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Proceedings Paper

Regularization approach for signal extrapolation with wavelets
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Paper Abstract

We propose a scale-limited signal model based on wavelet representation and study the reconstructability of scale-limited signals via extrapolation in this research. In analogy with the band-limited case, we define a scale-limited time-concentrated operator, and examine various vector spaces associated with such an operator. It is proved that the scale-limited signal space can be decomposed into the direct sum of two subspaces and only the component in one subspace can be exactly reconstructed, where the reconstructable subspace can be interpreted as a space consisting of scale/time-limited signals. Due to the ill-posedness of scale-limited extrapolation, a regularization process is introduced for noisy data extrapolation.

Paper Details

Date Published: 28 October 1994
PDF: 12 pages
Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); doi: 10.1117/12.190869
Show Author Affiliations
Li-Chien Lin, Univ. of Southern California (Taiwan)
C.-C. Jay Kuo, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 2296:
Advanced Signal Processing: Algorithms, Architectures, and Implementations V
Franklin T. Luk, Editor(s)

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