Returns the restored image, which is represented by
a matrix
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.
References
Qiu, P., and Kang, Y. "Blind Image Deblurring Using Jump Regression
Analysis," Statistica Sinica, 25, 2015, 879-899.