threeStage: Denoising, deblurring and edge-preserving

In DRIP: Discontinuous Regression and Image Processing

Denoising, deblurring and edge-preserving

Description

Estimate surface using local smoothing and fitting principal component line. Bandwidth is specified by user.

Usage

threeStage(image, bandwidth, edge1, edge2, 
blur = FALSE, plot = FALSE)

Arguments

image	A square matrix object of size n by n, no missing value allowed.
bandwidth	A positive integer that 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
blur	If blur = TRUE, besides a conventional 2-D kernel function, a univariate increasing kernel function is used in the local kernel smoothing to address the issue with blur.
plot	If plot = TRUE, the image of the fitted surface is plotted

Details

At each pixel, if there are step edges detected in the local neighborhood, a principal component line is fitted through the detected edge pixels to approximate the step edge locally and then the regression surface is estimated by a local constant kernel smoothing procedure using only the pixels on one side of the principal component line. If there are no step edges but roof/valley edges detected in the local neighborhood, the same procedure is followed except that the principal component line to fitted through the detected roof/valley edge pixels. In cases when there is either no step edges or roof/valley edges detected in the neighborhood, the regression surface at the pixel is estimated by the conventional local linear kernel smoothing procedure.

Value

Returns the restored image, which is represented by a matrix

References

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

See Also

JPLLK_surface, surfaceCluster

Examples

data(sar)
stepEdge <- stepEdgeLCK(sar, bandwidth=4, thresh=20)
stepEdge1 <- modify2(bandwidth=4, stepEdge)
fit <- threeStage(image=sar, bandwidth=4, edge1=stepEdge1, edge2=array(0, rep(ncol(sar), 2)))

DRIP documentation built on Sept. 1, 2023, 5:08 p.m.