cvwavelet.image.after.impute {CVThresh}R Documentation

Cross-Validation Wavelet Shrinkage for two-dimensional data after imputation

Description

This function performs level-dependent cross-validation wavelet shrinkage for two-dimensional data given the cross-validation scheme and imputation values.

Usage

cvwavelet.image.after.impute(images, imagewd, imageimpute,
   cv.index1=cv.index1, cv.index2=cv.index2,
   cv.optlevel=cv.optlevel, cv.tol=cv.tol, cv.maxiter=cv.maxiter,
   filter.number=10, family="DaubLeAsymm", thresh.type="soft", ll=3)

Arguments

images noisy image
imagewd two-dimensional wavelet transform
imageimpute two-dimensional imputed values according to cross-validation scheme
cv.index1 test dataset row index according to cross-validation scheme
cv.index2 test dataset column index according to cross-validation scheme
cv.optlevel thresholding levels
cv.tol tolerance for cross-validation
cv.maxiter maximum iteration for cross-validation
filter.number specifies the smoothness of wavelet in the decomposition (argument of WaveThresh)
family specifies the family of wavelets ``DaubExPhase" or ``DaubLeAsymm" (argument of WaveThresh)
thresh.type specifies the type of thresholding ``hard" or ``soft" (argument of WaveThresh)
ll specifies the lowest level to be thresholded

Details

Calculating thresholding values and reconstructing noisy image given cross-validation scheme and imputation.

Value

Reconstruction of images and thresholding values by level-dependent cross-validation

imagecv reconstruction of images
cvthresh thresholding values by level-dependent cross-validation

See Also

cvwavelet.image, cvtype.image, cvimpute.image.by.wavelet.


[Package CVThresh version 1.0.1 Index]