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Wavelet Denoising using Bayesian Thresholding and other methods

wdenoise object	WDenoise Object, Parameters and Functions
Wdenoise 1D Example using EBayesThresh	Example Code 1 : wdenoise (EBayesThresh)
Wdenoise 1D Example using fdrshrink	Example Code 2 : wdenoise
Image Denoising	Example Code 3 : Image Denoising using EBayesThresh and Visushrink

Thresholding Methods Implemented

EbayesThresh 1D/2D Bayesian Thresholding Method.

Minimaxshrink 1D/2D

VisuShrink 1D/2D

SureShrink 1D/2D

MODWTShrink 1D/2D

FDRShrink

Neighcoeffshrink

Neighblockshrink

Blockthreshshrink

References

Johnstone, I. M. and Silverman, B. W. (2004). Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences,Annals of Statistics,32, 1594–1649.

Johnstone, I. M. and Silverman, B. W. (2002). Empirical Bayes selection of wavelet thresholds, Technical Report, Department of Statistics, Stanford University.

ABRAMOVICH, F., BENJAMINI, Y., DONOHO, D. L. and JOHNSTONE, I. M. (2000). Adapting to unknown sparsity by controlling the false discovery rate. Technical Report 2000-19, Dept. Statistics, Stanford Univ

BENJAMINI, Y. and HOCHBERG, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B 57 289–300.

CAI, T. T. (2002). On block thresholding in wavelet regression: Adaptivity, block size, and threshold level. Statist. Sinica 12 1241–1273.

CAI, T. T. and SILVERMAN, B. W. (2001). Incorporating information on neighboring coefficients into wavelet estimation. Sankhy¯a Ser. B 63 127–148.

DONOHO, D. L. and JOHNSTONE, I. M. (1994). Minimax risk over p-balls for q -error. Probab. Theory Related Fields 99 277–303.

DONOHO, D. L. and JOHNSTONE, I. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, Volume 81, Issue 3, September 1994, Pages 425–455

DONOHO, D. L. and JOHNSTONE, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. J. Amer. Statist. Assoc. 90 1200–1224.