Wavelet-based denoising algorithms
There are three kinds of wavelet denoising methods. They are
modulus maximum method presented by Mallat et al., the relativity
method given by Xu and threshold method by Donoho et al. The above
methods all have their advantages and disadvantages, however, the
threshold denoising method is more widely used due to its simplicity.
Therefore, the threshold method is mainly discussed in this lecture.
The problems need to be solved and the possible trends are listed.
Furthermore, the feasible strategies are proposed.
References:
- Mallat S., and Zhong S. Characterization of signals from multiscale edges. IEEE Trans on PAMI, 1992, 14(7): 710~732
- Mallat S, Hwang W L. Singularity detection and processing with wavelets. IEEE Trans on IT, 1992, 38(2): 617~643
- Xu Y., Weaver J., Healy M., and et al. Wavelet transform
domain filters: A spatially selective noise filtration technique. IEEE
Trans. on IP, 1994, 3(6): 747-758
- Pan Q., Zhang L. Dai G. and et al. Two denoising methods by wavelet transform. IEEE Trans. on SP., 1999, 47(12): 3401-3406
- Donoho D.L. De-noising by Soft-thresholding. IEEE Trans on IT, 1995, 41(3): 613~627
- Donoho D L, Johnstone I M. Ideal spatial adaption via wavelet shrinkage. Biometrika, 1994, 81: 425~455
- Pizurica A., Philips W., Lemahieu I., and Acheeroy M. A joint
inter- and intrascale statistical model for Bayesian wavelet based
image denoising. IEEE Trans. on IP, 2002, 11(5): 545-557
- Balster E.J., Zheng Y.F., and Ewing R.L. Feature-based
wavelet shrinkage algorithm for image denoising. IEEE Trans. on IP,
2005, 14(12): 2024-2039
- Cai T.T., and Silverman B.W. Incorporating information on
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Statistics, 2001, 63(B): 127-148
- Chen G.Y., Bui T.D., and Krzyzak A. Image denoising using neighboring wavelet coefficients. ICASSP 2004, II:917-920