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.
KeywordsShrinkage Assure Convolution Sine
Unable to display preview. Download preview PDF.
- 8.Pollen D (1992) Daubechies’s scaling function on [0,3]. In Wavelets: A Tutorial in Theory and Applications, Chui CK, editor, Academic Press, New York, pp 3-13Google Scholar
- 9.Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical Recipes in Fortran: The Art of Scientific Computing, 2nd ed. Cambridge University Press, CambridgeGoogle Scholar
- 13.Wickerhauser MV (1992) Acoustic signal compression with wavelet packets. In Wavelets: A Tutorial in Theory and Applications, Chui CK, editor, Academic Press, New York, pp 679-700Google Scholar