stepEdgeLCK: Edge detection, denoising and deblurring

Description

Detect step edges in an image using piecewise local constant kernel smoothing.

Usage

stepEdgeLCK(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 ( ${\hat{f}}_{x}^{'}$ , ${\hat{f}}_{y}^{'}$ ). Then the one- sided local constant kernel (LCK) estimates are obtained in the two half neighborhoods respectively. The pixel is flagged as a step edge pixel if $| {\hat{f}}_{+} - {\hat{f}}_{-} | > u$ , where $u$ is a threshold value.

References

Kang, Y., and Qiu, P., "Jump Detection in Blurred Regression Surfaces," Technometrics, 56, 2014, 539-550.

Examples

Run this code

data(sar) # SAR image is bundled with the package and it is a 
          # standard test image in statistics literature.
edge <- stepEdgeLCK(image = sar, bandwidth = 4, thresh = 20)

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