(Enter summary)
Abstract: The more a priori knowledge we encode into a signal processing algorithm, the better performance we can expect.
In this paper, we overview several approaches to capturing the structure of singularities (edges, ridges, etc.) in
wavelet-based signal processing schemes. Leveraging results from approximation theory, we discuss nonlinear approximations
on trees and point out that an optimal tree approximant exists and is easily computed. The optimal
tree approximation inspires a new hierarchical... (Update)
Context of citations to this paper: More
...image features like edges cause high coecients at several resolutions. More sophisticated methods exploit this multiscale structure [37, 12, 3]. In the case of correlated noise [22] or a non orthogonal transform, the multiresolution structure of a wavelet transform justi es the...
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BibTeX entry: (Update)
R. Baraniuk. Optimal tree approximation with wavelets. In M. A. Unser, A. Aldroubi, and Laine A. F., editors, Wavelet Applications in Signal and Image Processing VII, volume 3813 of SPIE Proceedings, pages 206-214, July 1999. http://citeseer.ist.psu.edu/baraniuk99optimal.html More
@misc{ baraniuk99optimal,
author = "R. Baraniuk",
title = "Optimal tree approximation with wavelets",
text = "R. Baraniuk. Optimal tree approximation with wavelets. In M. A. Unser,
A. Aldroubi, and Laine A. F., editors, Wavelet Applications in Signal and
Image Processing VII, volume 3813 of SPIE Proceedings, pages 206-214, July
1999.",
year = "1999",
url = "citeseer.ist.psu.edu/baraniuk99optimal.html" }
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