Estimate surface using local pixel clustering and kernel smoothing. The bandwidth parameter is specified by the user.
surfaceCluster(image, bandwidth, sig.level, sigma, phi0,
mean_std_abs, cw=3, blur = FALSE, plot = FALSE)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.
A square matrix, no missing value allowed.
A positive integer that specifies the number of pixels to use in the local smoothing.
The significance level for the hypothesis test deciding whether to cluster pixels or not.
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.
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.
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.
The center weight for the center weighted median filter. It must be a positive integer.
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.
If plot = TRUE, the image of the fitted surface is plotted
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, 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, tools:::Rd_expr_doi("10.1007/s10463-007-0166-9").
JPLLK_surface, threeStage
data(brain)
fit <- surfaceCluster(image = brain, bandwidth = 4,
sig.level = .9995, cw = 3, blur = TRUE)
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