surfaceCluster: Denoising, deblurring and edge-preserving

Description Usage Arguments Value References See Also Examples

View source: R/surfaceCluster.r

Description

Estimate surface using local pixel clustering and kernel smoothing. Bandwidth is specified by user.

Usage

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surfaceCluster(image, bandwidth, sig.level, sigma, phi0, mean_std_abs, cw=3,
blur = FALSE, plot = FALSE)

Arguments

image

A square matrix object of size n by n, no missing value allowed.

bandwidth

A positive integer that specifies the number of pixels used in the local smoothing.

sig.level

Specifies the significance level of the hypothesis test deciding to cluster pixels or not.

sigma

Specifies the noise level (i.e., standard deviation of the error distribution). It is used for computing the asymptotic threshold for residuals, which are defined to be the difference between the local linear kernel smoothing output and the center weighted median filter output. If not specified by the user, a jump-preserving local linear kernel smoothing surface estimation (Qiu 2009) is used to obtain an estimated sigma.

phi0

Specifies the density of the standardized error distribution at 0. It is used for computing the asymptotic threshold for residuals, which are defined to be the difference between the local linear kernel smoothing output and the center weighted median filter output. If not specified by the user, a jump-preserving local linear kernel smoothing surface estimation (Qiu 2009) is used to obtain an estimated value.

mean_std_abs

Specifies the mean of absolute value of the standardized error. It is used for computing the asymptotic threshold for residuals, which are defined to be the difference between the local linear kernel smoothing output and the center weighted median filter output. If not specified by the user, a jump-preserving local linear kernel smoothing surface estimation (Qiu 2009) is used to obtain an estimated value.

cw

Specifies the center weight for the center weighted median filter. It must be a positive integer.

blur

If blur = TRUE, besides a conventional 2-D kernel function, a univariate increasing kernel function is used in the local kernel smoothing to address the issue with blur.

plot

If plot = TRUE, the image of the fitted surface is plotted

Value

Returns a list. 'estImg' is the restored image. 'sigma' is the estimated standard deviation of the random error. It is the input value if specified by the user. 'phi0' is the estimated density of the error distribution at 0. It is the input value if specified by the user. 'mean_std_abs' is the estimated absolute mean of the error distribution. It is the input value if specified by the user.

References

Kang, Y., Mukherjee, P.S., and Qiu, P. (2017), "Efficient Blind Image Deblurring Using Nonparametric Regression and Local Pixel Clustering", Technometrics, DOI: 10.1080/00401706.2017.1415975.

See Also

JPLLK_surface, threeStage

Examples

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data(brain) 
fit = surfaceCluster(image=brain, bandwidth=4, sig.level=.9995, cw=3, blur=TRUE)

Example output

Loading required package: readbitmap

DRIP documentation built on Oct. 23, 2020, 6:06 p.m.

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