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Published in:
2010 | OriginalPaper | Chapter
Wavelets
Author : Kenneth Lange
Published in: Numerical Analysis for Statisticians
Publisher: Springer New York
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Wavelets are just beginning to enter statistical theory and practice [2, 5, 7, 10, 12]. The pace of discovery is still swift, and except for orthogonal wavelets, the theory has yet to mature. However, the advantages of wavelets are already obvious in application areas such as image compression. Wavelets are more localized in space than the competing sines and cosines of Fourier series. They also use fewer coefficients in representing images, and they pick up edge effects better. The secret behind these successes is the capacity of wavelets to account for image variation on many different scales.