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=2, 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)
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 1.0.5 Index]