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The Lifting Scheme: a custom-design construction of biorthogonal wavelets Sweldens95, Sweldens 98 (appeared in SIAM Journal on Mathematical Analysis)

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Presentation on theme: "The Lifting Scheme: a custom-design construction of biorthogonal wavelets Sweldens95, Sweldens 98 (appeared in SIAM Journal on Mathematical Analysis)"— Presentation transcript:

1 The Lifting Scheme: a custom-design construction of biorthogonal wavelets Sweldens95, Sweldens 98 (appeared in SIAM Journal on Mathematical Analysis)

2 Relations of Biorthogonal Filters

3 Biorthogonal Scaling Functions and Wavelets Dual

4 Wavelet Transform (in operator notation) Note that up/down-sampling is absorbed into the filter operators Filter operators are matrices encoded with filter coefficients with proper dimensions transpose

5 Operator Notation

6 Relations on Filter Operators Biorthogonality Exact Reconstruction Write in matrix form:

7 Theorem 8 (Lifting) Take an initial set of biorthogonal filter operators A new set of biorthogonal filter operators can be found as Scaling functions and H and untouched

8 Proof of Biorthogonality

9 Choice of S Choose S to increase the number of vanishing moments of the wavelets Or, choose S so that the wavelet resembles a particular shape –This has important applications in automated target recognition and medical imaging

10 Corollary 6. Take an initial set of finite biorthogonal filters Then a new set of finite biorthogonal filters can be found as where s(  ) is a trigonometric polynomial Same thing expressed in frequency domain

11 Details

12 Theorem 7 (Lifting scheme) Take an initial set of biorthogonal scaling functions and wavelets Then a new set, which is formally biorthognal can be found as where the coefficients s k can be freely chosen. Same thing expressed in indexed notation

13 Dual Lifting Now leave dual scaling function and and G filters untouched

14 Fast Lifted Wavelet Transform Basic Idea: never explicitly form the new filters, but only work with the old filter, which can be trivial, and the S filter.

15 Before Lifting Forward Transform Inverse Transform

16 Examples Interpolating Wavelet Transform Biorthogonal Haar Transform

17 The Lazy Wavelet Subsampling operators E (even) and D (odd)

18 Interpolating Scaling Functions and Wavelets Interpolating filter: always pass through the data points Can always take Dirac function as a formal dual

19 Theorem 15 The set of filters resulting from interpolating scaling functions, and Diracs as their formal dual, can be seen as a dual lifting of the Lazy wavelet.

20

21 Algorithm of Interpolating Wavelet Transform (indexed form)

22 Example: Improved Haar Increase vanishing moments of the wavelets from 1 to 2 We have

23 Verify Biorthogonality Details

24 Improved Haar (cont)

25 g(0) = g’(0) = 0

26 Verify Biorthogonality Details


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