DRIP (version 2.0)

threeStage: Three-Stage Denoising and Deblurring

Description

Estimate jump location curves using local principal component lines. One-sided kernel smoothing is then used for surface estimation. Bandwidth is specified by the user.

Usage

threeStage(image, bandwidth, edge1, edge2, 
    blur = FALSE, plot = FALSE)

Value

The restored image, which is represented by a matrix.

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.

edge1

A matrix of 0 and 1 representing the detected step edge pixels

edge2

A matrix of 0 and 1 representing the detected roof/valley edge pixels

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

Details

At each pixel, if there are step edges detected in the local neighborhood, a principal component line is fitted through the detected edge pixels to approximate the step edge locally and then the regression surface is estimated by a local constant kernel smoothing procedure using only the pixels on one side of the principal component line. If there are no step edges but roof/valley edges detected in the local neighborhood, the same procedure is followed except that the principal component line to fitted through the detected roof/valley edge pixels. In cases when there is either no step edges or roof/valley edges detected in the neighborhood, the regression surface at the pixel is estimated by the conventional local linear kernel smoothing procedure.

References

Qiu, P. and Kang, Y. (2015) ``Blind Image Deblurring Using Jump Regression Analysis'', Statistica Sinica, 25, 879 -- 899, tools:::Rd_expr_doi("10.5705/ss.2014.054").

See Also

JPLLK_surface, surfaceCluster

Examples

step.edges <- stepEdge(sar, bandwidth = 4, thresh = 20, degree = 0)
stepEdge1 <- modify2(bandwidth = 4, step.edges)
fit <- threeStage(image = sar, bandwidth = 4, edge1 = stepEdge1,
    edge2 = array(0, rep(ncol(sar), 2)))