surfaceCluster: Jump-Preserving Surface Estimation Using Pixel Clustering

In DRIP: Discontinuous Regression and Image Processing

Jump-Preserving Surface Estimation Using Pixel Clustering

Description

Estimate surface using local pixel clustering and kernel smoothing. The bandwidth parameter is specified by the user.

Usage

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

Arguments

image	A square matrix, no missing value allowed.
bandwidth	A positive integer that specifies the number of pixels to use in the local smoothing.
sig.level	The significance level for the hypothesis test deciding whether to cluster pixels or not.
sigma	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	The density of the standardized error distribution at 0. It is used for computing the asymptotic threshold for residuals, whose definition is given above. 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	The mean of absolute value of the standardized error. It is used for computing the asymptotic threshold for residuals, whose definition is given above. 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	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

A list of 'estImg', the restored image, 'sigma', the estimated standard deviation of the random error, 'phi0', the estimated density of the error distribution at 0, and 'mean_std_abs', the estimated absolute mean of the error distribution.

References

Kang, Y., Mukherjee, P.S. and Qiu, P. (2017) "Efficient Blind Image Deblurring Using Nonparametric Regression and Local Pixel Clustering", Technometrics, 60(4), 522 – 531, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00401706.2017.1415975")}.

Qiu, P. (2009) "Jump-Preserving Surface Reconstruction from Noisy Data", Annals of the Institute of Statistical Mathematics, 61, 715 – 751, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10463-007-0166-9")}.

See Also

JPLLK_surface, threeStage

Examples

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

DRIP documentation built on May 29, 2024, 4:56 a.m.