roofEdgeParSel {DRIP} | R Documentation |
Select bandwidth and threshold value for the roof/valley edge detector using bootstrap procedure
roofEdgeParSel(image, bandwidth, thresh, nboot, edge1, blur=FALSE)
image |
A square matrix object of size n by n, no missing value allowed. |
bandwidth |
Positive integers to specify the number of pixels used in the local smoothing. These are the bandwidth parameters to be chosen from. |
thresh |
Threshold values to be chosen from. |
nboot |
Number of bootstrap samples. |
edge1 |
Step edges. The function excludes step edges when detect roof/valley edges. |
blur |
TRUE if the image contains blur, FALSE otherwise. |
If blur=TRUE, then a conventional local linear kernel smoothing is
applied to estimate the blurred surface; Bootstrap samples are obtained by
drawing with replacement from the residuals and the d_{KQ}
is computed
for the detected edges of the original sample and those of the bootstrap
samples. If blur=FALSE, the procedure is the same as when blur=TRUE
except that a jump-preserving kernel smoothing procedure is used to obtain
residuals.
Returns a list of the selected bandwdith, the selected threshold value,
and a matrix of d_{KQ}
values with each entry corresponding to each combination
of bandwdith and threshold.
Qiu, P., and Kang, Y. "Blind Image Deblurring Using Jump Regression Analysis," Statistica Sinica, 25, 2015, 879-899.
data(peppers) # Peppers image is bundled with the package and it is a
# standard test image in image processing literature.
#step.edges = stepEdgeLLK(image=peppers, bandwidth=9, thresh=17, plot=FALSE)
#parSel = roofEdgeParSel(image=peppers, bandwidth=5:6, thresh=
#c(5000,10000), nboot=10, edge1=step.edges, blur=FALSE) # Time Consuming