jpex: Blind Image Deblurring

In DRIP: Discontinuous Regression and Image Processing

Blind Image Deblurring

Description

jpex() takes in any square matrix (noisy blurry image) and deblurs it.

Usage

jpex(image, bandwidth, alpha, sigma)

Arguments

image	An input blurry image to deblurred. The input image is represented a square matrix.
bandwidth	A positive integer that specifies the size of the neighborhood for local smoothing.
alpha	A numberic between 0 and 1. This is the signifance level for the Chi-square hypothesis test that a given pixel is in a continuity region and not affected by the blur.
sigma	A positive numeric for the noise level in the blurred image. It is used in the Chi-square test.

Value

deblurred	The deblurred image
edge	The square matrix, the element of which is the value of the Chi-square test statistic at the pixel location. One can classify a given pixel as a blurry pixcel if edge[i,j]>qchisq(1-alpha, 2).

Author(s)

Yicheng Kang

References

Kang, Y. (2020) “Consistent Blind Image Deblurring Using Jump-Preserving Extrapolation”, Journal of Computational and Graphical Statistics, 29(2), 372 – 382.

See Also

cv.jpex

Examples

library(DRIP)
data(stopsign)
out <- jpex(image = stopsign, bandwidth = as.integer(2), sigma =
0.00623, alpha = 0.001)

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