threeStageParSel: image denoising/deblurring, bandwidth selection, bootstrap

In DRIP: Discontinuous Regression and Image Processing

image denoising/deblurring, bandwidth selection, bootstrap

Description

Select the bandwidth value for the image restoration method implemented in the function threeStage

Usage

threeStageParSel(image, bandwidth, edge1, edge2, nboot, blur=FALSE)

Arguments

image	A square matrix object of size n by n, no missing value allowed.
bandwidth	Bandwidth values to be chosen from. Each of these values need to be an positive integer which specifies the number of pixels used in the local smoothing.
edge1	A matrix of 0 and 1 of the same size as image represents detected step edge pixels.
edge2	A matrix of 0 and 1 of the same size as image represents detected roof/valley edge pixels.
nboot	Required when blur is TRUE. Unused when blur is FALSE. An positive integer to specify the number of bootstraps to perform. See Qiu and Kang (2015) Statistica Sinica for details.
blur	TRUE if the image contains blur, FALSE otherwise. If TRUE, the hybrid selection method proposed in Qiu and Kang (2015) Statistica Sinica is used. If FALSE, the leave-one-out cross validation is used.

Value

Returns a list of the selected bandwdith, and a matrix of CV values with each entry corresponding to each choice of bandwdith.

References

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

Examples

data(peppers) # Peppers image is bundled with the package and it is a
          # standard test image in image processing literature.
# Not run
#step.edges = stepEdgeLLK(peppers, 9, 17) # Step edge detection
#roof.edges = roofEdge(peppers, 6, 3000, edge1=step.edges) # Roof edge detection
#parSel = threeStageParSel(image=peppers, edge1=step.edges,
#edge2=roof.edges, bandwidth=4:5) # Time consuming

DRIP documentation built on April 7, 2022, 1:06 a.m.