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Wavelet domain image denoising via support vector regression

Wavelet domain image denoising via support vector regression Source: Electronics Letters,Volume:40,Issue: 2004 PP :1479-1481 Authors: H. Cheng, J.W. Tian, J. Liu and Q.Z. Yu Presented by: C.Y.Gun Date: 4/7 2005. Outline. Abstract Introduction( Mother Wavelet, Father Wavelet)

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Wavelet domain image denoising via support vector regression

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  1. Wavelet domain image denoising via support vector regression Source: Electronics Letters,Volume:40,Issue: 2004 PP :1479-1481 Authors: H. Cheng, J.W. Tian, J. Liu and Q.Z. Yu Presented by: C.Y.Gun Date: 4/7 2005

  2. Outline • Abstract • Introduction( Mother Wavelet, Father Wavelet) • Support vector regression • Proposed theory and algorithm • Experimental results and discussion

  3. Introduction Families of Wavelets: • Father wavelet ϕ(t) – generates scaling functions (low-pass filter) • Mother wavelet ψ(t) –generates wavelet functions (high-pass filter) • All other members of the wavelet family are scaling and translations of either the mother or the father wavelet

  4. Introduction • Father wavelets (low-pass filters)

  5. Introduction • Mother wavelets (high-pass filters)

  6. Introduction • 2-D DWT for Image

  7. Introduction • 2-D DWT for Image

  8. Introduction • 2-D DWT for Image

  9. Support vector regression • Standard linear regression equation • The linear case is a special case of the nonlinear regression equation

  10. Support vector regression • Idea : we define a « tube » of radius εaround the regression(ε≥ 0) • No error if y lays inside the « tube » or« band »

  11. Support vector regression • We therefore define an ε-insensitive loss function L1 • L2

  12. Support vector regression • Graphical representation

  13. Support vector regression Slack variables eiare defined for each observation: e e e e

  14. Support vector regression Kernel methods:

  15. Support vector regression Basic kernels for vectorial data: – Linear kernel: (feature space is Q-dimensional if Q is the dim of ; Map is identity!) – RBF-kernel: (feature space is infinite dimensional) – Polynomial kernel of degree two: (feature space is d(d+1)/2 -dimensional if d is the dim of )

  16. LS-SVM Regression We define the following optimization problem: Or:

  17. LS-SVM Regression From Least squares support vector machine classifiers ….(1)

  18. LS-SVM Regression The Result LS-SVM model for function estimation is ….(2)

  19. LS-SVM Regression

  20. LS-SVM Regression (1) ….(3)

  21. Proposed theory and algorithm Block matrix decompositions The main formula we need concerns the inverse of a block matrix =??

  22. Proposed theory and algorithm = = where

  23. Proposed theory and algorithm (3)

  24. Proposed theory and algorithm

  25. Proposed theory and algorithm H 3 3 SVR DWT V Original Image

  26. Proposed theory and algorithm where fmis the modified wavelet coefficient, p=0.3×max( f ) . Max( f ) is the maximal value of the wavelet coefficient in that detail subband.

  27. Experimental results and discussion

  28. Experimental results and discussion

  29. Reference • 1 Mallat, S.G.: ‘A theory for multiresolution signal decomposition: the wavelet representation’, IEEE Trans. Pattern Anal. Mach. Intell., 1989,11, (7), pp. 674–693 • 2 Donoho, D.L., and Johnstone, I.M.: ‘Ideal spatial adaptation via waveletshrinkage’, Biometrica, 1994, 81, pp. 425–455 • 3 Chang, S.G., Yu, B., and Vetterli, M.: ‘Adaptive wavelet thresholding forimage denoising and compression’, IEEE Trans. Image Process., 2000, 9,pp. 1532–1546 • 4 Vapnik, V.: ‘The nature of statistical learning theory’ (Springer-Verlag,New York, 1995) • 5 Suykens, J.A.K., and Vandewalle, J.: ‘Least squares support vectormachine classifiers’, Neural Process. Lett., 1999, 9, (3), pp. 293–300

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