cvwavelet.image {CVThresh}R Documentation

Wavelet reconstruction of image by level-dependent Cross-Validation

Description

This function reconstructs image by level-dependent cross-validation wavelet shrinkage.

Usage

cvwavelet.image(images, imagewd,
    cv.optlevel, cv.bsize=c(1,1), cv.kfold, cv.tol=0.1^3, cv.maxiter=100,
    impute.tol=0.1^3, impute.maxiter=100,
    filter.number=10, family="DaubLeAsymm", thresh.type="soft", ll=3)

Arguments

images noisy image
imagewd two-dimensional wavelet transform
cv.optlevel thresholding level
cv.bsize block size of cross-validation
cv.kfold the number of fold of cross-validation
cv.tol tolerance for cross-validation
cv.maxiter maximum iteration for cross-validation
impute.tol tolerance for imputation
impute.maxiter maximum iteration for imputation
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

This function performs level-dependent cross-validation wavelet shrinkage for two-dimensional data.

Value

imagecv reconstruction of image by level-dependent cross-validation wavelet shrinkage
cvthresh threshold values by level-dependent cross-validation

See Also

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

Examples

 
# Generate Noisy Lennon Image
data(lennon)
sdimage <- sd(as.numeric(lennon))
nlennon <- ncol(lennon); nlevel <- log2(ncol(lennon)); optlevel <- c(3:(nlevel-1))
set.seed(55)
lennonnoise <- lennon+matrix(rnorm(nlennon^2, 0, sdimage), nlennon, nlennon)

# Level-dependent Cross-validation Thresholding
lennonwd <- imwd(lennonnoise)
lennoncv <- cvwavelet.image(images=lennonnoise, imagewd=lennonwd,
                       cv.optlevel=optlevel, cv.bsize=c(1,1), cv.kfold=10)$imagecv
image(lennoncv, axes=FALSE, col=gray(0:100/100), main="Level-dependent CV")

[Package CVThresh version 1.0.1 Index]