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Wavelets and Operators
Abstract
Over the last two years, wavelet methods have shown themselves to be of considerable use to harmonic analysts and, in particular, advances have been made concerning their applications. The strength of wavelet methods lies in their ability to describe local phenomena more accurately than a traditional expansion in sines and cosines can. Thus, wavelets are ideal in many fields where an approach to transient behaviour is needed, for example, in considering acoustic or seismic signals, or in image processing. Yves Meyer stands the theory of wavelets firmly upon solid ground by basing his book on the fundamental work of Calderón, Zygmund and their collaborators. For anyone who would like an introduction to wavelets, this book will prove to be a necessary purchase.
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Citations
Cited by
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint.
Daubechies, I., Defrise, M., & De Mol, C. (2004) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20042 · Cited by 4,958
Fast wavelet transforms and numerical algorithms I
Beylkin, G., Coifman, R., & Rokhlin, V. (1991) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.3160440202 · Cited by 1,770
The Stationary Wavelet Transform and some Statistical Applications
Nason, G. P., & Silverman, B. W. (1995) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2544-7_17 · Cited by 1,122
Wavelet analysis for geophysical applications
Kumar, P., & Foufoula‐Georgiou, E. (1997) · Reviews of Geophysics · doi:10.1029/97rg00427 · Cited by 992
Metadata
- Authors
- Meyer, Y.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 1993-04-22
- Pages
- 256-365
- OpenAlex
- W574370508
- Cited by
- 2,034
- References
- 0
- FWCI
- 3.92
- Type
- book
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
