cvwavelet.image | R Documentation |
This function reconstructs image by level-dependent cross-validation wavelet shrinkage.
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)
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 |
This function performs level-dependent cross-validation wavelet shrinkage for two-dimensional data.
imagecv |
reconstruction of image by level-dependent cross-validation wavelet shrinkage |
cvthresh |
threshold values by level-dependent cross-validation |
cvtype.image
, cvimpute.image.by.wavelet
,
cvwavelet.image.after.impute
.
# 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(100:0/100), # main="Level-dependent CV")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.