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Multiresolution analysis and wavelet bases

Multiresolution analysis and wavelet bases. Outline : Multiresolution analysis The scaling function and scaling equation Orthogonal wavelets Biorthogonal wavelets Properties of wavelet bases A trous algorithm Pyramidal algorithm. The Continuous Wavelet Transform. wavelet.

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Multiresolution analysis and wavelet bases

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  1. Multiresolution analysis and wavelet bases Outline : • Multiresolution analysis • The scaling function and scaling equation • Orthogonal wavelets • Biorthogonal wavelets • Properties of wavelet bases • A trous algorithm • Pyramidal algorithm

  2. The Continuous Wavelet Transform • wavelet • decomposition

  3. The Continuous Wavelet Transform • Example : The mexican hat wavelet

  4. The Continuous Wavelet Transform • reconstruction • admissible wavelet : • simpler condition : zero mean wavelet Practically speaking, the reconstruction formula is of no use. Need for discrete wavelet transforms wich preserve exact reconstruction.

  5. The Haar wavelet • A basis for L2( R) : Averaging and differencing

  6. The Haar wavelet

  7. The Haar multiresolution analysis : • A sequence of embedded approximation subsets of L2( R) : with : • And a sequence of orthogonal complements, details’ subspaces : such that • is the scaling function. It’s a low pass filter. • a basis in is given by :

  8. The Haar multiresolution analysis Example :

  9. The Haar multiresolution analysis

  10. Two 2-scale relations : Defines the wavelet function.

  11. Orthogonal wavelet bases (1) • Find an orthogonal basis of : • Two-scale equations : • orthogonality requires : if k = 0, otherwise = 0 N : number of vanishing moments of the wavelet function

  12. Orthogonal wavelet bases (2) • Other way around , find a set of coefficients that satisfy the above equations. Since the solution is not unique, other favorable properties can be asked for : compact support, regularity, number of vanishing moments of the wavelet function. • then solve the two-scale equations. • Example : Daubechies seeks wavelets with minimum size compact support for any specified number of vanishing moments. The Daubechies D2 scaling and wavelet functions = ( )

  13. Orthogonal wavelet bases (2) • Other way around , find a set of coefficients that satisfy the above equations. Since the solution is not unique, other favorable properties can be asked for : compact support, regularity, number of vanishing moments of the wavelet function. • then solve the two-scale equations. • Example : Daubechies seeks wavelets with minimum size compact support for any specified number of vanishing moments. The Daubechies D2 scaling and wavelet functions Most wavelets we use can’t be expressed analytically.

  14. Fast algorithms (1) • we start with • we want to obtain • we use the following relations between coefficients at different scales: • reconstruction is obtained with :

  15. Fast algorithms using filter banks

  16. 2D Orthogonal wavelet transform

  17. 2D Orthogonal wavelet transform

  18. Example :

  19. Example :

  20. Biorthogonal Wavelet Transform :

  21. Biorthogonal Wavelet Transform : The structure of the filter bank algorithm is the same.

  22. Wavelet Packets

  23. Scale 5 Scale 3 Scale 1 Scale 2 Scale 4 WT h h h h h

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