stepEdgeLLK: Edge detection, denoising and deblurring

In DRIP: Discontinuous Regression and Image Processing

Edge detection, denoising and deblurring

Description

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

Usage

stepEdgeLLK(image, bandwidth, thresh, plot)

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.

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.

References

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

See Also

stepEdgeLCK, stepEdgeLC2K, stepEdgeLL2K, diffLLK

Examples

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)

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