https://github.com/cran/spatstat
Tip revision: 80e2bf6554e41a2e631d507470867f9586310633 authored by Adrian Baddeley on 10 January 2019, 15:30:04 UTC
version 1.58-2
version 1.58-2
Tip revision: 80e2bf6
blur.Rd
\name{blur}
\alias{blur}
\alias{Smooth.im}
\title{Apply Gaussian Blur to a Pixel Image}
\description{
Applies a Gaussian blur to a pixel image.
}
\usage{
blur(x, sigma = NULL, ..., normalise=FALSE, bleed = TRUE, varcov=NULL)
\method{Smooth}{im}(X, sigma = NULL, ...,
normalise=FALSE, bleed = TRUE, varcov=NULL)
}
\arguments{
\item{x,X}{The pixel image. An object of class \code{"im"}.}
\item{sigma}{
Standard deviation of isotropic Gaussian smoothing kernel.
}
\item{\dots}{
Ignored.
}
\item{normalise}{
Logical flag indicating whether the output values should be divided
by the corresponding blurred image of the window itself. See Details.
}
\item{bleed}{
Logical flag indicating whether to allow blur to extend outside the
original domain of the image. See Details.
}
\item{varcov}{
Variance-covariance matrix of anisotropic Gaussian kernel.
Incompatible with \code{sigma}.
}
}
\details{
This command applies a Gaussian blur to the pixel image \code{x}.
\code{Smooth.im} is a method for the generic \code{\link{Smooth}}
for pixel images. It is currently identical to \code{blur},
apart from the name of the first argument.
The blurring kernel is the isotropic Gaussian kernel with standard
deviation \code{sigma}, or the anisotropic Gaussian kernel with
variance-covariance matrix \code{varcov}.
The arguments \code{sigma} and \code{varcov} are incompatible.
Also \code{sigma} may be a vector of length 2 giving the
standard deviations of two independent Gaussian coordinates,
thus equivalent to \code{varcov = diag(sigma^2)}.
If the pixel values of \code{x} include some \code{NA} values
(meaning that the image domain does not completely fill
the rectangular frame) then these \code{NA} values are first reset to zero.
The algorithm then computes the convolution \eqn{x \ast G}{x * G}
of the (zero-padded) pixel
image \eqn{x} with the specified Gaussian kernel \eqn{G}.
If \code{normalise=FALSE}, then this convolution \eqn{x\ast G}{x * G}
is returned.
If \code{normalise=TRUE}, then the convolution \eqn{x \ast G}{x * G}
is normalised by
dividing it by the convolution \eqn{w \ast G}{w * G} of the image
domain \code{w}
with the same Gaussian kernel. Normalisation ensures that the result
can be interpreted as a weighted average of input pixel values,
without edge effects due to the shape of the domain.
If \code{bleed=FALSE}, then pixel values outside the original image
domain are set to \code{NA}. Thus the output is a pixel image with the
same domain as the input. If \code{bleed=TRUE}, then no such
alteration is performed, and the result is a pixel image defined
everywhere in the rectangular frame containing the input image.
Computation is performed using the Fast Fourier Transform.
}
\value{
A pixel image with the same pixel array as the input image \code{x}.
}
\seealso{
\code{\link{interp.im}} for interpolating a pixel image to a finer resolution,
\code{\link{density.ppp}} for blurring a point pattern,
\code{\link{Smooth.ppp}} for interpolating marks attached to points.
}
\examples{
data(letterR)
Z <- as.im(function(x,y) { 4 * x^2 + 3 * y }, letterR)
par(mfrow=c(1,3))
plot(Z)
plot(letterR, add=TRUE)
plot(blur(Z, 0.3, bleed=TRUE))
plot(letterR, add=TRUE)
plot(blur(Z, 0.3, bleed=FALSE))
plot(letterR, add=TRUE)
par(mfrow=c(1,1))
}
\author{\adrian
and \rolf
}
\keyword{spatial}
\keyword{nonparametric}