jpex: Blind Image Deblurring

In DRIP: Discontinuous Regression and Image Processing

Blind Image Deblurring

Description

Take in any square matrix (noisy blurry image) and deblur it.

Usage

jpex(image, bandwidth, alpha, sigma)

Arguments

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

Value

deblurred	A square matrix representing the deblurred image.
edge	A square matrix, the element of which is the value of the Chi-square test statistic at a pixel location. One can classify a given pixel as a blurry pixel 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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2019.1665536")}.

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 May 29, 2024, 4:56 a.m.