fun.cvwavelet.image.after.impute {CVThresh} | R Documentation |
This function performs level-dependent cross-validation wavelet shrinkage for two-dimensional data given the cross-validation scheme and imputation values.
fun.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)
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 |
Calculating thresholding values and reconstructing noisy image given cross-validation scheme and imputation.
Reconstruction of images and thresholding values by level-dependent cross-validation
imagecv |
reconstruction of images |
cvthresh |
thresholding values by level-dependent cross-validation |
fun.cvwavelet.image
, fun.cvtype.image
, fun.cvimpute.image.by.wavelet
.