DRIP (version 1.6)

roofEdgeParSel: roof/valley edge detection, parameter selection

Description

Select bandwidth and threshold value for the roof/valley edge detector using bootstrap procedure

Usage

roofEdgeParSel(image, bandwidth, thresh, nboot, edge1, blur=FALSE)

Arguments

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.

Value

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.

Details

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.

References

Qiu, P., and Kang, Y. "Blind Image Deblurring Using Jump Regression Analysis," Statistica Sinica, 25, 2015, 879-899.

See Also

roofDiff, roofEdge

Examples

# NOT RUN {
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
# }