Uses example images to inpaint or approximate an existing image.

Employs a robust regression approach to learn the relationship between a sample image and a list of images that are mapped to the same space as the sample image. The regression uses data from an image neighborhood.

exemplarInpainting(
  img,
  paintMask,
  imageList,
  featureRadius = 2,
  scaleInpaintIntensity = 0,
  sharpen = FALSE,
  feather = 1,
  predalgorithm = "lm",
  debug = FALSE
)

Arguments

img

antsImage to be approximated / painted
paintMask

painting mask with values 1 or values 1 and 2 - if there is a 2 then it will learn from label 1 to paint label 2. should cover the brain.
imageList

a list containing antsImages
featureRadius

- radius of image neighborhood e.g. 2
scaleInpaintIntensity

- brighter or darker painted voxels, default of 0 sets this parameter automatically
sharpen

- sharpen the approximated image
feather

- value (e.g. 1) that helps feather the mask for smooth blending
predalgorithm

- string svm or lm
debug

- TRUE or FALSE

Value

inpainted image

Examples


set.seed(123)
fi<-abs(replicate(100, rnorm(100)))
fi[1:10,]<-fi[,1:10]<-fi[91:100,]<-fi[,91:100]<-0
mask<-fi
mask[ mask > 0 ]<-1
mask2<-mask
mask2[11:20,11:20]<-2
mask<-as.antsImage( mask , "float" )
fi<-as.antsImage( fi , "float" )
fi<-smoothImage(fi,3)
mo<-as.antsImage( replicate(100, rnorm(100)) , "float" )
mo2<-as.antsImage( replicate(100, rnorm(100)) , "float" )
ilist<-list(mo,mo2)
painted<-exemplarInpainting(fi,mask,ilist)
mask2<-as.antsImage( mask2 , "float" )
painted2<-exemplarInpainting(fi,mask2,ilist)
# just use 1 image, so no regression is performed
painted3<-exemplarInpainting(fi,mask2, list(ilist[[1]]))