stepEdgeLLK: Edge detection, denoising and deblurring
Description
Detect step edges in an image using piecewise
local linear kernel smoothing.
Usage
stepEdgeLLK(image, bandwidth, thresh, plot)
Value
Returns a matrix of zeros and ones of the same size as
image. Value one represent edge pixels and value zero represent
non-edge pixels.
Arguments
image
A square matrix object of size n by n, no
missing value allowed.
bandwidth
A positive integer to specify the number of
pixels used in the local smoothing.
thresh
Threshold value used in the edge detection
criterion.
plot
If plot = TRUE, an image of detected edges is
plotted.
Details
At each pixel, the gradient is estimated by a local linear
kernel smoothing procedure. Next, the local neighborhood is
divided into two halves along the direction perpendicular to
(\(\widehat{f}'_{x}\), \(\widehat{f}'_{y}\)). Then the one-
sided local linear kernel (LLK) estimates are obtained in the
two half neighborhoods respectively. The pixel is flagged as
a step edge pixel if \(|\widehat{f}_+ - \widehat{f}_-|>u\), where
\(u\) is a threshold value.
References
Kang, Y., and Qiu, P., "Jump Detection in Blurred Regression
Surfaces," Technometrics, 56, 2014, 539-550.
data(sar) # SAR image is bundled with the package and it is a # standard test image in statistics literature.edge <- stepEdgeLLK(image = sar, bandwidth = 9, thresh = 17)