Package 'adimpro' reference manual

Title:	Adaptive Smoothing of Digital Images
Description:	Implements tools for manipulation of digital images and the Propagation Separation approach by Polzehl and Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1> for smoothing digital images, see Polzehl and Tabelow (2007) <DOI:10.18637/jss.v019.i01>.
Authors:	Karsten Tabelow [aut, cre], Joerg Polzehl [aut]
Maintainer:	Karsten Tabelow <[email protected]>
License:	GPL (>= 2)
Version:	0.9.7.2
Built:	2024-12-25 06:52:08 UTC
Source:	CRAN

I/O Functions

Description

Create image objects of class "adimpro" from arrays, RAW-format files and other image formats.

Usage

read.raw(filename, type="PPM",
         wb="CAMERA",cspace="Adobe",interp="Bilinear",maxrange=TRUE,
         rm.ppm=TRUE, compress=TRUE)
read.image(filename, compress=TRUE)
make.image(x,compress=TRUE, gammatype="None", whitep = "D65", 
         cspace="Adobe", scale="Original",xmode="RGB")
read.raw(filename, type="PPM",
         wb="CAMERA",cspace="Adobe",interp="Bilinear",maxrange=TRUE,
         rm.ppm=TRUE, compress=TRUE)
read.image(filename, compress=TRUE)
make.image(x,compress=TRUE, gammatype="None", whitep = "D65", 
         cspace="Adobe", scale="Original",xmode="RGB")

Arguments

`filename`	file name
`x`	Array or matrix containing RGB or greyscale values in the range (0,1) or (0,65535).
`type`	option settings for dcraw. default "PPM". `type="png"` allows to read greyvalue png images as RAW-data (used as internal solution to store RAW information)
`wb`	white balance. default "CAMERA"
`cspace`	defines the output color space, default "sRGB" (sRGB D65), alternatives are "RAW" (Camera specific), "Adobe" (Adobe 1998 D65), "wGamut" (Wide Gamut D65), "kodak" (Kodak ProPhoto D65) and "XYZ", see manpages of dcraw.
`interp`	defines the interpolation method, default "Bilinear", Alternatives are "VNG", "AHD", "FourC" (Four color interpolation) and "Halfsize", see manpages of dcraw. "VNG" seems to provide the smallest spatial correlations.
`maxrange`	If TRUE increase range of values to maximum.
`rm.ppm`	remove intermediate tmp file? default `TRUE`
`gammatype`	character, determines the type of gamma correction within the image. "ITU" stands for ITU-R BT.709-3 as e.g. used by `dcraw`. Alternatives recognized within the package are "None", "sRGB" and "CIE" (CIE L*). Please specify if you know that your image is not gamma corrected using ITU-R BT.709-3.
`whitep`	White point in `xyY` space. Can be given as one of (character) `c("A","B","C","E","D50","D55","D65","D75","F2","F7","F11")` or as a two element numeric vector of chromatic `xy` coordinates. `"D65"` corresponds to the default white point of `"sRGB"` and `"Adobe"` RGB-spaces.
`compress`	logical, determines if image data are stored in raw-format.
`scale`	`"Original"` scales to `(0,max(img$img))` if `min(img$img)<0`, otherwise keeps the original scale. `"Maxcontrast"` scales each channel to maximum contrast
`xmode`	`xmode` determines how to interpret the values in `x` if `length(dim(x))==3`. Implemented are `xmode="RGB"` (default) and `xmode="HSI"`

Details

If ImageMagick is available on the system, read.image reads any of the following image file formats: c("tif", "tiff", "pgm", "ppm", "png", "pnm", "gif", "jpg", "jpeg") converts it into a temporary "pgm" or "ppm" file. This file is removed after reading the image. If ImageMagick is not available only "pgm", "ppm" and "pnm" formats can be processed.

If dcraw is available on the system, read.raw reads many RAW formats. type sets options to dcraw: "PPM" sets "-4", "RAW" sets "-4 -d", "HALFSIZE" sets "-h", "INFO" sets "-i -v". wb indicates, which white balance should be used: "NONE", "AUTO", "CAMERA".

Functions read.raw(file,type="RAW") and read.image(file) provide identical results on png-mages. If the result is a color, greyvalued or RAW image depends on the contend of the comment associated with the png-image.

make.image converts an appropriate 2 or 3 dimensional array to an image object of class "adimpro".

Value

object of class "adimpro" containing the image. The object has the following components:

`img`	array containing the color values in the color space specified by `value$type`.
`type`	the color space.
`depth`	color depth, here "16bit".
`dim`	vector of length 2 containing the number of pixel in horizontal and vertival direction.
`file`	the argument `file` identifying the image.
`cspace`	the type of rgb space used, as specified by `cspace`.
`interp`	interpolation applied by dcraw, as specified by `interp`.
`gamma`	has a gamma correction been applied, here FALSE for `read.raw` and TRUE for `read.image`
`gammatype`	type of gamma correction `read.image.`
`wb`	type of white balance, as specified by `wb`.
`compressed`	image data are stored as raw-vector (TRUE) or array of integers (FALSE).

Note

The function read.raw requires dcraw to be installed.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(io)
## Not run: demo(io)

Set parameters for graphical display

Description

On systems capabale of X11 the function sets the X11-type (preferably "Xlib"). It also sets a default size for graphical displays opended by functions from the package.

Usage

adimpro.options(xsize = NULL, ysize = NULL)
adimpro.options(xsize = NULL, ysize = NULL)

Arguments

`xsize`	display width in pt
`ysize`	display height in pt

Details

The function assignes the specified values to as a list to the variable name ".adimpro". This variable is, if it exists, evaluated by several other functions.

On some systems the default X11.options()$type ”cairo” leads to significant slower image display. You may try to use X11.options(type=''Xlib'') instead. To automatically choose this option set the system environment variable R_X11type (setenv R_X11type Xlib or export R_X11type=Xlib) before loading the package.

Value

returns invisible(NULL)

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Image Processing

Description

Color space transformations, change of white balance and exposure, gamma correction and histogram equalization.

Usage

adjust.image(img, gammatype=NULL, cspace = NULL, whitep = NULL, 
                 temp = NULL, black=0, exposure = 1, kind = "Bradford", 
                 alg = 1, compress= TRUE)
adjust.image(img, gammatype=NULL, cspace = NULL, whitep = NULL, 
                 temp = NULL, black=0, exposure = 1, kind = "Bradford", 
                 alg = 1, compress= TRUE)

Arguments

`img`	image object, class"adimpro".
`gammatype`	character, determines the type of gamma correction within the image. "ITU" stands for ITU-R BT.709-3 as e.g. used by `dcraw`. Alternatives recognized within the package are "None", "sRGB" and "CIE" (CIE L*). `NULL` keeps the actual setting. `gammatype="histogram"` forces histogram equalization based on the corresponding greyvalue image.
`cspace`	defines the output color space, default "sRGB" (sRGB D65), alternatives are "Adobe" (Adobe 1998 D65), "wGamut" (Wide Gamut D65), "kodak" (Kodak ProPhoto D65) "xyz", "yuv", "yiq" and "hsi". `NULL` keeps the actual setting.
`whitep`	White point in `xyY` space. Can be given as one of (character) `c("A","B","C","E","D50","D55","D65","D75","F2","F7","F11")` or as a two element numeric vector of chromatic `xy` coordinates. `"D65"` corresponds to the default white point of `"sRGB"` and `"Adobe"` RGB-spaces. `NULL` keeps the actual setting.
`temp`	Color temperature. Can be used to specify chromatic `xy` coordinates of the whitepoint. Only used if `is.null(whitep)`.
`black`	Adjustment for black color. Color values with luminance <= black will be assigned to black in RGB. Adjustment ist done in xyY space.
`exposure`	Multiplicative factor for all color channels (in xyz or rgb spaces). Applied in linear color space, i.e. if the image is gamma corrected the gamma correction is reversed first.
`kind`	Algorithm for chromatic adaptation. Alternatives are `"Bradford"`, `"VonKries"` and `"XYZscaling"`
`alg`	determines the approximation for the gamma correction. Select 1 for fastest computation and 3 for maximum accuracy, or 2 for a compromise.
`compress`	logical, determines if image data are stored in raw-format.

Details

This function adjusts color channels and applies gamma correction (if applicable).

If color.par$red or color.par$blue or color.par$brightness differ from 1.0 the corresponding channels are multiplied with the provided values. Saturated values are set to 1.

If img$gamma==FALSE, perform gamma correction with color.par$ga and color.par$bp. alg chooses between three different computing algorithms (approximations) with increasing computation time and precision (alg is 1,2, or 3).

Value

Adjusted image object of class "adimpro".

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(color)
## Not run: demo(color)

Propagation-Separation approach for smoothing of 2D images

Description

This functions implement the Propagation-Separation approach (local constant and local polynomial model) for smoothing images. Function awsaniso uses anisotropic location weights. This is done by evaluating local gradient estimates obtained from the actual estimated color values.

Usage

awsimage(object, hmax=4, aws=TRUE, varmodel=NULL, ladjust=1.25,
         mask=NULL, xind = NULL, yind = NULL,
         wghts=c(1,1,1,1), scorr=TRUE,
         lkern="Plateau", plateau=NULL, homogen=TRUE, earlystop=TRUE,
         demo=FALSE, graph=FALSE,
         max.pixel=4.e2, clip = FALSE, compress=TRUE)
awspimage(object, hmax=12, aws=TRUE, degree=1, varmodel = NULL,
          ladjust=1.0, xind = NULL, yind = NULL, 
          wghts=c(1,1,1,1), scorr= TRUE,
          lkern="Plateau", plateau=NULL, homogen=TRUE, earlystop=TRUE,
          demo=FALSE, graph=FALSE, 
          max.pixel= 4.e2, clip = FALSE, compress=TRUE)
awsaniso(object, hmax = 4, g = 3, rho = 0, aws = TRUE, varmodel = NULL,
          ladjust = 1, xind = NULL, yind = NULL, wghts = c(1, 1, 1, 1), 
          scorr = TRUE, lkern = "Triangle", demo = FALSE, graph = FALSE,
          satexp = 0.25, max.pixel = 400, clip = FALSE, compress = TRUE)
awsimage(object, hmax=4, aws=TRUE, varmodel=NULL, ladjust=1.25,
         mask=NULL, xind = NULL, yind = NULL,
         wghts=c(1,1,1,1), scorr=TRUE,
         lkern="Plateau", plateau=NULL, homogen=TRUE, earlystop=TRUE,
         demo=FALSE, graph=FALSE,
         max.pixel=4.e2, clip = FALSE, compress=TRUE)
awspimage(object, hmax=12, aws=TRUE, degree=1, varmodel = NULL,
          ladjust=1.0, xind = NULL, yind = NULL, 
          wghts=c(1,1,1,1), scorr= TRUE,
          lkern="Plateau", plateau=NULL, homogen=TRUE, earlystop=TRUE,
          demo=FALSE, graph=FALSE, 
          max.pixel= 4.e2, clip = FALSE, compress=TRUE)
awsaniso(object, hmax = 4, g = 3, rho = 0, aws = TRUE, varmodel = NULL,
          ladjust = 1, xind = NULL, yind = NULL, wghts = c(1, 1, 1, 1), 
          scorr = TRUE, lkern = "Triangle", demo = FALSE, graph = FALSE,
          satexp = 0.25, max.pixel = 400, clip = FALSE, compress = TRUE)

Arguments

`object`	Image object, class "adimpro", as from `read.image`, `read.raw`, or `make.image`.
`hmax`	Maximum bandwidth to use in the iteration procedure.
`g`	Bandwidth for anisotropic smoothing gradient estimates, preferably $g \ge 3$ for images with line type texture and small $g \approx 1$ for improving edges between homogeneous regions (function `awsaniso` only).
`rho`	Regularization parameter for anisotropic smoothing gradient estimates, preferably $\rho = 0$ for images with line type texture and large $\rho \approx 3$ for improving edges between homogeneous regions. (function `awsaniso` only)
`aws`	(logical). If `TRUE` the propagation - separation (PS) approach from Polzehl and Spokoiny (2006) is used. `aws=FALSE` turns off the statistical penalty resulting in a nonadaptive kernel estimate using a kernel with bandwidth `hmax`.
`degree`	Degree of the local polynomial model for `awspimage`. 0, 1, or 2 only.
`varmodel`	`varmodel` specifies how variances are to be estimated. This can be a homogeneous variance estimate (`varmodel="None"`) assuming uncorrelated errors (both spatial and between channels). Alternatives are an adaptive homogeneous or linear (function of the mean) variance estimate that depends on estimated correlations and on residuals from the last iteration step. The default `varmodel=NULL` corresponds to `varmodel == "Linear"` if `img$gamma==FALSE` and `varmodel == "Constant"` otherwise.
`ladjust`	adjustment factor for lambda (>=1). Default values for lambda are selected for Gaussian distributions and default settings of parameters `lkern` and `plateau`. Skewed or heavy tailed distributions may require slightly larger values for lambda to meet the propagation condition. `ladjust` allows to increase lambda in such situations.
`mask`	logical array of the same size as the image or `NULL` (default). Smoothing is restricted to the smallest rectangle including all pixel where `mask==TRUE` and restricts computations to these pixel. This need not be a connected area (Typical usage: smooth all bright regions)! Only used if `is.null(xind)` `&&` `is.null(yind)`. Inactive if `mask==NULL`. Can only be used if `varmodel="None"`.
`xind`, `yind`	Restrict smoothing to rectangular area defined by pixel indices `xind,yind` in x- and y-direction. Full range if `NULL` (default).
`wghts`	allows to weight the information from different (up to 4) color channels. The weights are used in the statistical penalty of the PS-procedure. Note that lambda-values are selected for `wghts==c(1,1,1,1)`, please use parameter `ladjust` to set an appropriate value.
`scorr`	(logical). Specifies whether spatial correlation is to be estimated. Defaults to `TRUE`. Is set to `FALSE` if `mask` is not `NULL`.
`lkern`	Specifies the location kernel. Defaults to "Triangle", other choices are "Quadratic", "Cubic" and "Uniform". The use of "Triangle" corresponds to the Epanechnicov kernel nonparametric kernel regression.
`plateau`	Extension of the plateau in the statistical kernel. Can take values from (0,1), defaults to `0.25`.
`homogen`	If TRUE the algorithm determines, in each design point i, a circle of maximum radius, such that the statistical penalty `s_{ij}` for all points j within the circle is less than the value specified in `plateau`. In subsequent iteration steps the statistical penalty for such points is set to zero. This is only used if `plateau>0`. This results in more stable intermediate estimates and in a smoother reconstruction. `homogen=TRUE` leads to increased memory requirements.
`earlystop`	If TRUE the algorithm determines, in each design point i, a circle of minimal radius, such that the circle includes all point j with positive weights `w_{ij}`. if this radius is considerably smaller than the actual bandwidth then the estimate in point i is fixed. This should considarably reduce computing time in case of large `hmax`.`earlystop=TRUE` slightly increases memory requirements.
`demo`	(logical). If `demo=TRUE` the function pauses after each iteration. Defaults to `FALSE`.
`graph`	(logical). If `graph=TRUE` intermediate results are illustrated after each iteration step. Defaults to `FALSE`.
`max.pixel`	Maximum dimension of images for display if `graph=TRUE`. If the true dimension is larger, the images are downscaled for display. See also `show.image`.
`satexp`	exponent used for scaling saturation in anisotropy visualization (function `awsaniso` only)
`clip`	(logical). If `TRUE` a clipping region is selected, see `clip.image`, using the information contained in `xind` or `yind`. If both are `NULL` a clipping region can be defined by left mouse clicks. The image `object` is reduced to the clipping region before smoothing.
`compress`	logical, determines if image data are stored in raw-format.

Details

The function implements the Propagation-Separation (PS) approach to nonparametric smoothing (formerly introduced as Adaptive Weights Smoothing) for varying coefficient likelihood (awsimage) and local polynomial (awspimage) models for greyscale and color images.

The distribution of grey (color) values is considered to be Gaussian. Noise can be colored.

The numerical complexity of the procedure is mainly determined by hmax. The number of iterations is 2*log(hmax)/log(1.25). Comlexity in each iteration step is Const*hakt*n with hakt being the actual bandwith in the iteration step and n the number of pixels. hmax determines the maximal possible variance reduction.

All other parameters of the approach only depend on the specified values for skern/lkern and are therefore set internally to meaningful default values.

For a detailed description of the procedure see references below.

The script used to control the values of parameter lambda is stored in directory inst/adjust.

Value

Object of class "adimpro"

`img`	Contains the reconstructed image.
`ni`	Contains the sum of weights, i.e. `trace(W_i)`, in all grid points `i`.
`ni0`	Contains the maximum sum of weights for an nonadaptive kernel estimate with the same bandwidth.
`hmax`	Bandwidth used in the last iteration.
`call`	The arguments of the function call.
`varcoef`	Estimated coefficients in the variance model for the color channels, if `varmodel` is "Constant" or "Linear".
`wghts`	The weights used for the color channels.
`scorr`	Estimated spatial correlations for each channel, if `scorr=TRUE`
`chcorr`	Estimated correlations between color channels, if `scorr=TRUE`

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

References

Polzehl and Spokoiny (2006). Propagation-Separation Approach for Local Likelihood Estimation. Probability Theory and Related Fields. 3 (135) 335 - 362.

Polzehl and Spokoiny (2005). Structural adaptive smoothing adaptive smoothing by Propagation-Separation-methods. WIAS-Preprint No. 1068.

Polzehl, J. and Tabelow, K. (2007). Adaptive smoothing of digital images, Journal of Statistical Software 19 (1).

Examples

## Not run: demo(awsimage)
## Not run: demo(awsimage)

Smoothing and demosaicing of RAW images

Description

The function integrates smoothing and demosaicing of RAW image data.

Usage

awsraw(object, hmax = 4, aws = TRUE, wb = c(1, 1, 1), cspace = "Adobe", 
       ladjust = 1, maxrange=TRUE, lkern = "Triangle", graph = FALSE, 
       max.pixel = 400, compress = TRUE)
awsraw(object, hmax = 4, aws = TRUE, wb = c(1, 1, 1), cspace = "Adobe", 
       ladjust = 1, maxrange=TRUE, lkern = "Triangle", graph = FALSE, 
       max.pixel = 400, compress = TRUE)

Arguments

`object`	an object of class `adimpro` containing the RAW image data. See `read.raw` for creating such objects.
`hmax`	maximal bandwidth to use in the smoothing algorithm.
`aws`	use adaptive weights if `aws==TRUE`.
`wb`	Vector containing factors for the three color chanels, allows to change the white balance.
`cspace`	Color space of the result,
`ladjust`	Factor for the critical value $\lambda$ . Defaults to `1`, smaller values increase sensitivity but may result in isolated noisy pixel. Values larger than `1` give smoother up to cartoon like results.
`maxrange`	If TRUE increase range of values to maximum.
`lkern`	Specifies the location kernel. Defaults to "Triangle", other choices are "Quadratic", "Cubic" and "Uniform". The use of "Triangle" corresponds to the Epanechnicov kernel nonparametric kernel regression.
`graph`	(logical). If `graph=TRUE` intermediate results are illustrated after each iteration step. Defaults to `FALSE`.
`max.pixel`	Maximum dimension of images for display if `graph=TRUE`. If the true dimension is larger, the images are downscaled for display. See also `show.image`.
`compress`	logical, determines if image data are stored in raw-format.

Details

Adaptive smoothing is performed on the original RAW data, restricting positive weights to pixel corresponding to the same color channel. Noise is assumed to have a variance depending linearly on the mean. Weights are determined by weigthed distances between color vectors. These color vectors are obtained by demosaicing that is applied to the smoothed RAW data after each iteration of the smoothing algorithm. The demosaicing algorithm is a 3D generalized median, see method="Median4" in function develop.raw.

Value

Object of class "adimpro"

`img`	Contains the reconstructed image.
`ni`	Contains the sum of weights, i.e. `trace(W_i)`, in all grid points `i`.
`ni0`	Contains the maximum sum of weights for an nonadaptive kernel estimate with the same bandwidth.
`hmax`	Bandwidth used in the last iteration.
`call`	The arguments of the function call.
`varcoef`	Estimated coefficients in the linear variance model for the color channels.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

References

Polzehl, J. and Tabelow, K. (2007). Adaptive smoothing of digital images, Journal of Statistical Software 19 (1).

Examples

## Not run: demo(raw)
## Not run: demo(raw)

Create an image by clipping

Description

The function allows to define a clipping region by arguments xind and yind or interactively by mouseclicks. A new image is created by cutting out the clipping region.

Usage

clip.image(img, xind = NULL, yind = NULL, compress=NULL, ...)
clip.image(img, xind = NULL, yind = NULL, compress=NULL, ...)

Arguments

`img`	Object of class "adimpro" as created by `read.image`, `read.raw` or `make.image`.
`xind`	`xind` defines the horizontal extension of the clipping region.
`yind`	`yind` defines the vertical extension of the clipping region.
`compress`	image data are stored as raw-vector (TRUE) or array of integers (FALSE). `compress=NULL` keeps the format used in img.
`...`	additional arguments to `show.image` can be passed here.

Details

If both xind==NULL and yind==NULL, show.image is called and the clipping region can be set by two left mouse clicks to opposite corners.

Value

An object of class "adimpro".

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(manipulate)
## Not run: demo(manipulate)

Color Space Conversion

Description

Color space conversion functions for RGB, YUV, YIQ, XYZ, and HSI.

Usage

rgb2grey(obj, compress=TRUE)
rgb2hsi(obj)
hsi2rgb(obj, cspace = "Adobe", compress=TRUE)
rgb2yuv(obj)
yuv2rgb(obj, cspace = "Adobe", compress=TRUE)
rgb2yiq(obj)
yiq2rgb(obj, cspace = "Adobe", compress=TRUE)
rgb2xyz(obj)
xyz2rgb(obj, cspace = "Adobe", black= 0, exposure=1, compress=TRUE)
rgb2grey(obj, compress=TRUE)
rgb2hsi(obj)
hsi2rgb(obj, cspace = "Adobe", compress=TRUE)
rgb2yuv(obj)
yuv2rgb(obj, cspace = "Adobe", compress=TRUE)
rgb2yiq(obj)
yiq2rgb(obj, cspace = "Adobe", compress=TRUE)
rgb2xyz(obj)
xyz2rgb(obj, cspace = "Adobe", black= 0, exposure=1, compress=TRUE)

Arguments

`obj`	an object of class "adimpro", `obj$type` should coincide with the first three letters of the function name, that is `obj$img` containes color values in the corresponding color space.
`cspace`	Target color space, one of `c("sRGB","Adobe","wGamut","kodak","xyz")`. For function `xyz2rgb` also `c("yuv","yiq")` are implemented.
`exposure`	Multiplicative factor for all color channels (in xyz or rgb spaces). Applied in linear color space, i.e. if the image is gamma corrected the gamma correction is reversed first.
`black`	Adjustment for black color. Color values with luminance <= black will be assigned to black in RGB.
`compress`	logical, determines if image data are returned in raw-format.

Details

The functions convert an image obj from one color space into another.

rgb2grey converts from RGB to GREYSCALE.

rgb2hsi and hsi2rgb convert from RGB to HSI and vice versa.

rgb2yuv and yuv2rgb convert from RGB to YUV and vice versa.

rgb2yiq and yiq2rgb convert from RGB to YIQ and vice versa.

rgb2xyz and xyz2rgb convert from RGB to CIE XYZ and vice versa.

Conversion to XYZ, YIQ, YUV and HSI involves an inverse gamma correction if required.

Value

an object of class "adimpro", with value$type specifying the color space (last three letters of the function name or 'greyscale' for rgb2grey) and value$img containing the color values.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

References

Gonzalez, R.C., and Woods, R.E. (2002). Digital Image Processing. Prentice Hall.

Polzehl, J., and Tabelow, K. (2007). Adaptive smoothing of digital images, Journal of Statistical Software 19 (1).

Examples

## Not run: demo(color)
## Not run: demo(color)

Pixelwise operations on a pair of images

Description

The function allows to perform pixelwise operations, specified by a supplied function, on a pair of images.

Usage

combine(img1, img2, fun = "+", rescale = TRUE, compress = TRUE, gammatype = "None",
        whitep = "D65", cspace = "Adobe", xmode = "RGB", ...)
combine(img1, img2, fun = "+", rescale = TRUE, compress = TRUE, gammatype = "None",
        whitep = "D65", cspace = "Adobe", xmode = "RGB", ...)

Arguments

`img1`	image, object of class adimpro
`img2`	image, object of class adimpro, need to have the same dimension as img1
`fun`	A function or primitive of two (or more) arguments specifying the operation. The first argument corresponds to grey/color-values in img1, the second to img2. Auxiliary parameters can passed throug `"..."`
`rescale`	logical: if TRUE the resulting image is rescaled to fit into the range of possible grey/color-values, if FALSE values outside the range are truncated.
`compress`	logical, determines if image data are stored in raw-format.
`gammatype`	character, determines the type of gamma correction within the image. "ITU" stands for ITU-R BT.709-3 as e.g. used by `dcraw`. Alternatives recognized within the package are "None", "sRGB" and "CIE" (CIE L*). Please specify if you know that your image is not gamma corrected using ITU-R BT.709-3.
`whitep`	White point in `xyY` space. Can be given as one of (character) `c("A","B","C","E","D50","D55","D65","D75","F2","F7","F11")` or as a two element numeric vector of chromatic `xy` coordinates. `"D65"` corresponds to the default white point of `"sRGB"` and `"Adobe"` RGB-spaces.
`cspace`	defines the output color space, default "sRGB" (sRGB D65), alternatives are "RAW" (Camera specific), "Adobe" (Adobe 1998 D65), "wGamut" (Wide Gamut D65), "kodak" (Kodak ProPhoto D65) and "XYZ", see manpages of dcraw.
`xmode`	`xmode` determines how to interpret the values in `x` if `length(dim(x))==3`. Implemented are `xmode="RGB"` (default) and `xmode="HSI"`
`...`	additional parameters for function `fun`

Details

There are two mayor applications for this function. First it allows to add noise to an image by first creating an image that contains the noise and then adding this image using fun="+". Second it offers a way to replace parts of an image, see examples.

Value

object of class "adimpro" containing the image. The object has the following components:

`img`	array containing the color values in the color space specified by `value$type`.
`type`	the color space.
`depth`	color depth, here "16bit".
`dim`	vector of length 2 containing the number of pixel in horizontal and vertival direction.
`file`	the argument `file` identifying the image.
`cspace`	the type of rgb space used, as specified by `cspace`.
`interp`	interpolation applied by dcraw, as specified by `interp`.
`gamma`	has a gamma correction been applied, here FALSE for `read.raw` and TRUE for `read.image`
`gammatype`	type of gamma correction `read.image.`
`wb`	type of white balance, as specified by `wb`.
`compressed`	image data are stored as raw-vector (TRUE) or array of integers (FALSE).

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(combine)
## Not run: demo(combine)

Create a color image from RAW image data.

Description

The function generates a color image from RAW image data.

Usage

develop.raw(object, method = "BILINEAR", wb = c(1, 1, 1), maxrange= TRUE, compress = TRUE)
develop.raw(object, method = "BILINEAR", wb = c(1, 1, 1), maxrange= TRUE, compress = TRUE)

Arguments

`object`	An object of class `"adimpro"` containing RAW image data, e.g. created by function `read.raw`. Such an object is characterized by `object$type=="RAW"`.
`method`	Method to fill missing color values. Currently implemented are `method="HALF"` (reduce image size by factor of 2), `method="FULL"` (use color from neighbor within (2x2) Bayer mask)), `method="BILINEAR"` (bilinear interpolation), `method="Median4"` and `method="Median16"`, the latter two being based on L1-MM over shifted Bayer masks. `method="Median16"` delivers much smoother results, but is considerably slower then the other methods.
`wb`	numerical vector of length 3 containing multiplicative factors for the three color channels.
`maxrange`	If TRUE increase range of values to maximum.
`compress`	logical, determines if image data are stored as `raw`.

Details

RAW image data usually contain only one color value at each pixel, with colors arranged in a so called Bayer mask. Converting RAW images into color images requires to fill the missing entries in the color channels.

Value

object of class "adimpro" containing the image.

Note

The function requires dcraw to be installed.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(io)
## Not run: demo(raw)
## Not run: demo(io)
## Not run: demo(raw)

Image Processing

Description

Edge detection using Laplacian, Sobel, or Robert Cross filter.

Usage

edges(img, type = "Laplacian", ltype=1, abs=FALSE)
edges(img, type = "Laplacian", ltype=1, abs=FALSE)

Arguments

`img`	an object of class "adimpro".
`type`	type of edges detection filter. "Laplacian" (default), "Sobel" , or "Robertcross".
`ltype`	type of laplacian filter. 1,2,3, or 4
`abs`	take absolute values of results. This has only an effect for `tyoe="Laplacian"`

Details

This function applies the Laplacian, Sobel, or Robert Cross filter to the input image img. The filter is applied to each color channel separately. ltype determines the different matrices for Laplacian filter used in the literature. ltype == 1 will use:

conv <- matrix(c(-1,-1,-1,-1,-1, -1,-1,-1,-1,-1, -1,-1,24,-1,-1, -1,-1,-1,-1,-1, -1,-1,-1,-1,-1),5,5)

ltype == 2 will use:

conv <- matrix(c(0,-1,0,-1,4,-1,0,-1,0), 3, 3)

ltype == 3 will use:

conv <- matrix(c(-1,-1,-1,-1,8,-1,-1,-1,-1), 3, 3)

ltype == 4 (default) will use:

conv <- matrix(c(1,-2,1,-2,4,-2,1,-2,1), 3, 3)

Value

Array containing the values for the edge detector in each pixel and color channel.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

References

Gonzalez, R.C., and Woods, R.E. (2002). Digital Image Processing. Prentice Hall.

Examples

## Not run: demo(manipulate)
## Not run: demo(awspimage)
## Not run: demo(manipulate)
## Not run: demo(awspimage)

Extract image data from adimpro object

Description

Extract image data from adimpro object

Usage

extract.image(object)
extract.image(object)

Arguments

object

adimpro object

Value

array or matrix of image data

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(io)
## Not run: demo(manipulate)
## Not run: demo(io)
## Not run: demo(manipulate)

Extract EXIF information and additional characteristics from an object of class "adimpro".

Description

Extract EXIF information and additional characteristics from an object of class "adimpro".

Usage

extract.info(object, what = "Bayer")
extract.info(object, what = "Bayer")

Arguments

object

an object of class "adimpro" or a character string that was written as a comment by functions write.image or write.raw.

what

A character string specifying which information is to be extracted. Currently implemented are "Bayer" (Bayer mask), "Daymulti" (Daylight multiplyers), "Cammulti" (camera multipliers), "Camera" (camera model), "Isize" (Image size), "Osize" (image output size), "File" (source filename), "Interpolation"(Interpolation used to convert RAW to RGB), "Gammatype" (type of gamma correction), "WhiteBalance" (white balance), "WhitePoint" (white point), "Type" (image type), "xind" (horizontal subindex used by function clip.image) and "yind" (vertical subindex used by function clip.image).

Details

This function is mainly used to access and manipulate information when reading or developing images. A more comprehensive overview is provided by function summary.

Value

A character string or integer vector depending on the argument "what".

Note

ImageMagick has to be installed on the system to write "tif", "tiff", "png", "gif", "jpg" or "jpeg" files.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

extract information about effective size of neighborhoods

Description

The function allows to extract information about the effective size of neighborhoods used in each pixel from objects generated by awsimage or awspimage. The result is converted into a greyscale image.

Usage

extract.ni(object, gammatype = "ITU", compress = TRUE)
extract.ni(object, gammatype = "ITU", compress = TRUE)

Arguments

`object`	object returned from `awsimage` or `awspimage`.
`gammatype`	character, determines the type of gamma correction within the image. "ITU" stands for ITU-R BT.709-3 as e.g. used by `dcraw`. Alternatives recognized within the package are "None", "sRGB" and "CIE" (CIE L*).
`compress`	logical, if `TRUE` the returned image will be compressed.

Value

an object of class "adimpro".

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

create an image that visualizes anisotropy

Description

The function creates an object of class adimpro that visualizes anisotropy information using the HSI color space for main direction of anisotropy (H), maximum eigenvalue^satexp (S) and log(excentricity) (I).

Usage

imganiso2D(x, satexp = 0.25, g=3, rho=0)
imganiso2D(x, satexp = 0.25, g=3, rho=0)

Arguments

`x`	a field of 2D tensors (Dimension `c(3,n1,n2)`) or an object of class `"adimpro"`.
`satexp`	exponent for maximum eigenvalue in saturation channel. Determines the contrast in this channel.
`g`	Bandwidth for anisotropic smoothing gradient estimates, preferably $g \ge 3$ for images with line type texture and small $g \approx 1$ for improving edges between homogeneous regions.
`rho`	Regularization parameter for anisotropic smoothing gradient estimates, preferably $\rho = 0$ for images with line type texture and large $\rho \approx 3*variance$ for improving edges between homogeneous regions.

Value

an object (image) of class adimpro.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

References

Polzehl, J. and Tabelow, K. (2007). Adaptive smoothing of digital images, Journal of Statistical Software 19 (1).

Examples

## Not run: demo(awsimage)
## Not run: demo(manipulate)
## Not run: demo(awsimage)
## Not run: demo(manipulate)

Create a mask for use within function awsimage

Description

Select part of image according to greyscale (or color) value.

Usage

mask.create(img,
            range1 = c(0, 1), range2 = c(0, 1), range3 = c(0, 1),
            locate = TRUE)
mask.create(img,
            range1 = c(0, 1), range2 = c(0, 1), range3 = c(0, 1),
            locate = TRUE)

Arguments

`img`	Image object of class "adimpro", usually the result returned from `read.image`, `read.raw`, or `make.image`.
`range1`	Range of gray values or values in the first color channel. `mask` is set to `FALSE` for all pixel with values outside this range. Defaults to `c(0,1)`.
`range2`	Range of values in the second color channel. `mask` is set to `FALSE` for all pixel with values outside this range. Defaults to `c(0,1)`.
`range3`	Range of values in the third color channel. `mask` is set to `FALSE` for all pixel with values outside this range. Defaults to `c(0,1)`.
`locate`	(logical). If `TRUE` (default), the image is displayed and two opposite corners of a rectangular region can be selected using the mouse (call of `locator(2)`).

Details

Function to create a mask. The returned object mask can be used to restrict computations in function awsimage to a region characterized by mask==TRUE.

Value

logical matrix of image dimension.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

References

Polzehl, J., and Tabelow, K. (2007). Adaptive smoothing of digital images, Journal of Statistical Software 19 (1).

I/O functions

Description

Visualize image data.

Usage

## S3 method for class 'adimpro'
plot(x, new=FALSE, gammatype = NULL, cspace = NULL,
                 whitep = NULL, temp = NULL, black = 0, exposure = 1,...)
## S3 method for class 'adimpro'
plot(x, new=FALSE, gammatype = NULL, cspace = NULL,
                 whitep = NULL, temp = NULL, black = 0, exposure = 1,...)

Arguments

`x`	image object of class "adimpro"
`new`	should new X11() be opened? default `FALSE`
`gammatype`	character, determines the type of gamma correction within the image. "ITU" stands for ITU-R BT.709-3 as e.g. used by `dcraw`. Alternatives recognized within the package are "None", "sRGB" and "CIE" (CIE L*). `NULL` keeps the actual setting. `gammatype="histogram"` forces histogram equalization based on the corresponding greyvalue image.
`cspace`	defines the output color space, default "sRGB" (sRGB D65), alternatives are "Adobe" (Adobe 1998 D65), "wGamut" (Wide Gamut D65), "kodak" (Kodak ProPhoto D65) "xyz", "yuv", "yiq" and "hsi". `NULL` keeps the actual setting.
`whitep`	White point in `xyY` space. Can be given as one of (character) `c("A","B","C","E","D50","D55","D65","D75","F2","F7","F11")` or as a two element numeric vector of chromatic `xy` coordinates. `"D65"` corresponds to the default white point of `"sRGB"` and `"Adobe"` RGB-spaces. `NULL` keeps the actual setting.
`temp`	Color temperature. Can be used to specify chromatic `xy` coordinates of the whitepoint. Only used if `is.null(whitep)`.
`black`	Adjustment for black color. Color values with luminance <= black will be assigned to black in RGB. Adjustment ist done in xyY space.
`exposure`	Multiplicative factor for all color channels (in xyz or rgb spaces). Applied in linear color space, i.e. if the image is gamma corrected the gamma correction is reversed first.
`...`	not used

Details

This functions shows information on the image. This includes histograms of color values in each channel of the specified (x$type) color space, a thumbnail (in "sRGB" with gamma correction gammatype if specified and gammatype="ITU" elsewhere), some information on the image and, if x was produced by awsimage, an image illustrating the local adaptation.

Value

nothing is returned.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(color)
## Not run: demo(color)

Slighty enhanced image function.

Description

The function builds upon function from package graphics, but allows to change some of it's defaults. These changes can be made through rimage.options for subsequent calls of rimage or by direct specification in ....

Usage

rimage(x = seq(0, 1, length.out = nrow(z)), y = seq(0, 1, length.out = ncol(z)), z, ...)
rimage.options(...)
rimage(x = seq(0, 1, length.out = nrow(z)), y = seq(0, 1, length.out = ncol(z)), z, ...)
rimage.options(...)

Arguments

x, y

locations of grid lines at which the values in 'z' are measured. These must be finite, non-missing and in (strictly) ascending order. By default, equally spaced values from 0 to 1 are used. If 'x' is a 'list', its components 'x$x' and 'x$y' are used for 'x' and 'y', respectively. If the list has component 'z' this is used for 'z'. (Same as for function image)

z

a numeric or logical matrix containing the values to be plotted ('NA's are allowed). Note that 'x' can be used instead of 'z' for convenience. (Same as for function image)

...

The following arguments can be supplied to both rimage and rimage.options:

zquantiles - numeric(2): quantiles of image intensity values to be usd to determine ,zlim as zlim <- quantile(z, zquantiles). not used if zlim is supplied directly. default c(0,1).
up - color for intensity values larger than zlim[2], default ”white”.
low - color for intensity values smaller than zlim[1], default ”black”.
NAcolor - color for intensity NA values, default 0 (transparent).
col - color scheme for values in the range of zlim, default grey(0:255/255).
asp - aspect ratio, default TRUE.
xlab - name for x-axis, default ”x”
ylab - name for y-axis, default ”y”
xaxt - axis type for x-axis, default ”s”, see par.
yaxt - axis type for y-axis, default ”s”, see par.
bty - type of box to draw, default ”o”, see par.
swapx - swap x axis, default FALSE
swapy - swap y axis, default FALSE

Additionally all parameters that can be passed to function image via ... can be used with function rimage.

Details

This function exists just for convenience to be used if sequences of images are to be plotted using the same settings / style. Function rimage.options uses an hidden object .rimage within an evironment .adimproOpts in the space of package adimpro to store the options.

Value

Both functions return invisible(NULL).

Author(s)

Joerg Polzehl [email protected]

Image Processing

Description

Rotate an image by 0, 90, 180 or 270 degrees.

Usage

rotate.image(img, angle = 90, compress=NULL)
rotate.image(img, angle = 90, compress=NULL)

Arguments

`img`	image data, that is an object of class "adimpro".
`angle`	0, 90, 180 or 270 degrees
`compress`	store result as compressed image if `compress=TRUE`. `compress=NULL` uses the same format as the original object.

Details

The function rotates the image img by 0, 90, 180 or 270 degrees. Any other value for degree will cause an exception. The returned object contains an additional component rotate determining the rotation.

Value

An object of class "adimpro" containing the rotated image.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(manipulate)
## Not run: demo(manipulate)

Segmentation by AWS

Description

The function allows to segment an image into two or three level sets.

Usage

segment(object, level=0.5, delta = 0, thresh = 3, fov = NULL, channel = 0, 
        hmax = 4, aws = TRUE, varmodel = NULL, ladjust = 1.25, xind = NULL, 
        yind = NULL, wghts = c(0.299, 0.587, 0.114, 0), scorr = TRUE, 
        lkern = "Triangle", plateau = NULL, homogen = TRUE, 
        earlystop = TRUE, demo = FALSE, select = FALSE, sext = 1.4, 
        connected = FALSE, graph = FALSE, max.pixel = 400, compress = TRUE)
segment(object, level=0.5, delta = 0, thresh = 3, fov = NULL, channel = 0, 
        hmax = 4, aws = TRUE, varmodel = NULL, ladjust = 1.25, xind = NULL, 
        yind = NULL, wghts = c(0.299, 0.587, 0.114, 0), scorr = TRUE, 
        lkern = "Triangle", plateau = NULL, homogen = TRUE, 
        earlystop = TRUE, demo = FALSE, select = FALSE, sext = 1.4, 
        connected = FALSE, graph = FALSE, max.pixel = 400, compress = TRUE)

Arguments

`object`	Image object, class "adimpro", as from `read.image`, `read.raw`, or `make.image`.
`level`	center of gray/color-values of the second segment, will not be used if `select=TRUE`. May be specified such that either `level-delta` and `level+delta` are within the interval (0,1) or such that they are within the interval (0,65535) (2 Byte integers).
`delta`	half width of gray/color-values of the second segment, nay be increased if `select=TRUE`. May be specified such that either `level-delta` and `level+delta` are within the interval (0,1) or such that they are within the interval (0,65535) (2 Byte integers).
`thresh`	Critical value for final assignment to segment 1 or 3 , should be specified as a quantile of the standard Gaussian distribution.
`fov`	size of field of view in pixel
`channel`	specifies which information to use for segmentation. 0: use grey valued image obtained from color images, 1-3: use the specified color channel.
`hmax`	Maximum bandwidth to use in the iteration procedure.
`aws`	(logical). If `TRUE` the propagation - separation (PS) approach from Polzehl and Spokoiny (2006) is used. `aws=FALSE` turns off the statistical penalty resulting in a nonadaptive kernel estimate using a kernel with bandwidth `hmax`.
`varmodel`	`varmodel` specifies how variances are to be estimated. This can be a homogeneous variance estimate (`varmodel="None"`) assuming uncorrelated errors (both spatial and between channels). Alternatives are an adaptive homogeneous or linear (function of the mean) variance estimate that depends on estimated correlations and on residuals from the last iteration step. The default `varmodel=NULL` corresponds to `varmodel == "Linear"` if `img$gamma==FALSE` and `varmodel == "Constant"` otherwise.
`ladjust`	adjustment factor for lambda (>=1). Default values for lambda are selected for Gaussian distributions. Skewed or heavy tailed distributions may require slightly larger values for lambda to meet the propagation condition. `ladjust` allows to increase lambda in such situations.
`xind`, `yind`	Restrict smoothing to rectangular area defined by pixel indices `xind,yind` in x- and y-direction. Full range if `NULL` (default).
`wghts`	allows to weight the information from different (up to 4) color channels. The weights are used in the statistical penalty of the PS-procedure.
`scorr`	(logical). Specifies whether spatial correlation is to be estimated. Defaults to `TRUE`. Is set to `FALSE` if `mask` is not `NULL`.
`lkern`	Specifies the location kernel. Defaults to "Triangle", other choices are "Quadratic", "Cubic" and "Uniform". The use of "Triangle" corresponds to the Epanechnicov kernel nonparametric kernel regression.
`plateau`	Extension of the plateau in the statistical kernel. Can take values from (0,1), defaults to `0.25`.
`homogen`	If TRUE the algorithm determines, in each design point i, a circle of maximum radius, such that the statistical penalty `s_{ij}` for all points j within the circle is less than the value specified in `plateau`. In subsequent iteration steps the statistical penalty for such points is set to zero. This is only used if `plateau>0`. This results in more stable intermediate estimates and in a smoother reconstruction. `homogen=TRUE` leads to increased memory requirements.
`earlystop`	If TRUE the algorithm determines, in each design point i, a circle of minimal radius, such that the circle includes all point j with positive weights `w_{ij}`. if this radius is considerably smaller than the actual bandwidth then the estimate in point i is fixed. This should considarably reduce computing time in case of large `hmax`.`earlystop=TRUE` slightly increases memory requirements.
`demo`	(logical). If `demo=TRUE` the function pauses after each iteration. Defaults to `FALSE`.
`select`	if TRUE a homogeneous rectangular region can be specified interactively. A value of `level` is the generated as the median of values within the selected region.
`sext`	if `select==TRUE` the value of delta is increased by `sext` times the standard deviation (estimated by IQR) of the values in the selected region.
`connected`	if TRUE the set of pixel within the same segment connected to the specified pixel is extracted.
`graph`	(logical). If `graph=TRUE` intermediate results are illustrated after each iteration step. Defaults to `FALSE`.
`max.pixel`	Maximum dimension of images for display if `graph=TRUE`. If the true dimension is larger, the images are downscaled for display. See also `show.image`.
`compress`	logical, determines if image data are stored in raw-format.

Details

The image is segmented into three parts by performing multiscale tests of the hypotheses H1 value >= level - delta and H2 value <= level + delta. Pixel where the first hypotesis is rejected are classified as -1 (segment 1) while rejection of H2 results in classification 1 (segment 3). Pixel where neither H1 or H2 are rejected ar assigned to a value 0 (segment 2). Critical values for the tests are adjusted for smoothness at the different scales inspected in the iteration process using results from multiscale testing, see e.g. Duembgen and Spokoiny (2001). Critical values also depend on the size of the region of interest specified in parameter fov.

Within segment 2 structural adaptive smoothing is performed while if a pair of pixel belongs to segment 1 or segment 3 the corresponding weight will be nonadaptive.

If connected==TRUE pixel in segment 2 0 are reassigned to a value 2 if they belong to a maximal connected subset of segment2 that contains the center of the specified homogeneous set.

Value

Object of class "adimpro" with

img

containing a greyvalued image with 3 or 4 levels corresponding to the identified segments.

and additional list elements

`hsegm`	containing the maximal bandwidth used
`level`	the value of parameter `level` used
`delta`	the value of parameter `delta` used
`thresh`	the value of parameter `thresh` used

Note

This function is still experimental and may be changes considerably in future.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

References

Duembgen, L. and Spokoiny, V. (2001). Multiscale testing of qualitative hypoteses. Ann. Stat. 29, 124–152.

Polzehl, J. and Spokoiny, V. (2006). Propagation-Separation Approach for Local Likelihood Estimation. Probability Theory and Related Fields. 3 (135) 335 - 362.

Examples

## Not run: demo(segment)
## Not run: demo(segment)

I/O functions

Description

Display an image on the screen.

Usage

show.image(img, max.x = 1000, max.y = 1000, gammatype = "ITU", 
           whitep = NULL, temp = NULL, cspace = "sRGB", black=0, exposure = 1, 
           channel=NULL, new = FALSE,  ...)
show.image(img, max.x = 1000, max.y = 1000, gammatype = "ITU", 
           whitep = NULL, temp = NULL, cspace = "sRGB", black=0, exposure = 1, 
           channel=NULL, new = FALSE,  ...)

Arguments

`img`	image data, an object of class "adimpro".
`max.x`	maximum value of pixels in x dimension to be displayed.
`max.y`	maximum value of pixels in y dimension to be displayed.
`gammatype`	character, determines the type of gamma correction within the image. "ITU" stands for ITU-R BT.709-3 as e.g. used by `dcraw`. Alternatives recognized within the package are "None", "sRGB" and "CIE" (CIE L*). `gammatype="histogram"` forces histogram equalization based on the corresponding greyvalue image.
`cspace`	defines the output color space, default "sRGB" (sRGB D65), alternatives are "Adobe" (Adobe 1998 D65), "wGamut" (Wide Gamut D65), "kodak" (Kodak ProPhoto D65) "xyz", "yuv", "yiq" and "hsi". `NULL` keeps the actual setting. If `color.space%in%c("hsi","yuv","yiq","xyz")` the individual channels are rescaled to provide maximum contrast. Information from the three channels is coded as "red", "green" and "blue" providing a miscolored image in these cases. Individual channels can be displayed as greyvalue images by specifying the `channel` argument. `color.space="greyvalue"` provides a greyvalue image.
`whitep`	White point in `xyY` space. Can be given as one of (character) `c("A","B","C","E","D50","D55","D65","D75","F2","F7","F11")` or as a two element numeric vector of chromatic `xy` coordinates. `"D65"` corresponds to the default white point of `"sRGB"` and `"Adobe"` RGB-spaces. `NULL` keeps the actual setting.
`temp`	Color temperature. Can be used to specify chromatic `xy` coordinates of the whitepoint. Only used if `is.null(whitep)`.
`black`	Adjustment for black color. Color values with luminance <= black will be assigned to black in RGB. Adjustment ist done in xyY space.
`exposure`	Multiplicative factor for all color channels (in xyz or rgb spaces). Applied in linear color space, i.e. if the image is gamma corrected the gamma correction is reversed first.
`channel`	allows to select a color channel (1: red, 2: green, 3: blue in case of "rgb") for display.
`new`	should new X11() be opened? default `FALSE`
`...`	additional arguments to `image` can be passed here.

Details

This function displayes greyscale and color images on the screen. If the actual dimension of the image exceeds max.x or max.y the image is shrinked by displaying only part of the pixels (every second/third/... such that the resulting dimension is smaller than max.x or max.y)

Value

Nothing is returned

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(io)
## Not run: demo(io)

Image Processing

Description

Shrink resolution of an image

Usage

shrink.image(img, method = "median",
             xt = img$dim[1], yt = img$dim[2], ratio = TRUE,
             compress=TRUE)
shrink.image(img, method = "median",
             xt = img$dim[1], yt = img$dim[2], ratio = TRUE,
             compress=TRUE)

Arguments

`img`	image data, an object of class "adimpro".
`method`	method to be used to shrink the image. "median" (default), "mean", or "nearest". "median" is supposed to give best results. For a considerably faster result use "nearest".
`xt`	target x-dimension
`yt`	target y-dimension
`ratio`	logical. preserve x-y ratio? default: `TRUE`
`compress`	logical, determines if image data are stored in raw-format.

Details

This function shrinkes the resolution of the image such that the x-y dimension of the resulting image is smaller than the original one. xt and yt give the target dimension of the image. If ratio == TRUE (default) the ratio between x- and y-dimension is preserved.

method "nearest" selects a pixel nearest to the new coordinates. method "mean" defines the color of a pixel as the mean of all pixel identified with the new coordinate. method "median" set the color of a pixel as color of an L1-generalized median of all pixel identified with the new coordinate.

Value

shrinked image

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(manipulate)
## Not run: demo(manipulate)

I/O functions

Description

'summary' method for class '"adimpro"'.

Usage

## S3 method for class 'adimpro'
summary(object, ...)
## S3 method for class 'adimpro'
summary(object, ...)

Arguments

`object`	an object of class `adimpro`, usually, a result of a call to `read.raw`, or `read.image`.
`...`	further arguments passed to or from other methods.

Details

The method tries to print information on the image, like image dimension, color space, value range, etc.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(io)
## Not run: demo(manipulate)
## Not run: demo(io)
## Not run: demo(manipulate)

I/O Functions

Description

Write an image file.

Usage

write.image(img, file = "tmp.ppm", max.x=NULL, max.y=NULL, depth = NULL, gammatype="ITU", 
        whitep = NULL, temp = NULL, cspace = NULL, black = 0, exposure = 1)
write.image(img, file = "tmp.ppm", max.x=NULL, max.y=NULL, depth = NULL, gammatype="ITU", 
        whitep = NULL, temp = NULL, cspace = NULL, black = 0, exposure = 1)

Arguments

`img`	image data, an object of class "adimpro".
`file`	file name, the extension determines the image file format.
`max.x`	maximum value of pixels in x dimension.
`max.y`	maximum value of pixels in y dimension.
`depth`	color depth, either 8 or 16 (bit)! The value is reset to 8 if the specified image file format does not allow for 16 Bit encoding.
`gammatype`	character, determines the type of gamma correction within the image. "ITU" stands for ITU-R BT.709-3 as e.g. used by `dcraw`. Alternatives recognized within the package are "None", "sRGB" and "CIE" (CIE L*). `NULL` keeps the actual setting. `gammatype="histogram"` forces histogram equalization based on the corresponding greyvalue image.
`cspace`	defines the output color space, default is `img$cspace` can be set to "sRGB", "Adobe" (Adobe 1998), "wGamut" (Wide Gamut), "kodak" (Kodak ProPhoto) "xyz", "yuv", "yiq" and "hsi"
`whitep`	White point in `xyY` space. Can be given as one of (character) `c("A","B","C","E","D50","D55","D65","D75","F2","F7","F11")` or as a two element numeric vector of chromatic `xy` coordinates. `"D65"` corresponds to the default white point of `"sRGB"` and `"Adobe"` RGB-spaces. `NULL` keeps the actual setting.
`temp`	Color temperature. Can be used to specify chromatic `xy` coordinates of the whitepoint. Only used if `is.null(whitep)`.
`black`	Adjustment for black color. Color values with luminance <= black will be assigned to black in RGB. Adjustment ist done in xyY space.
`exposure`	Multiplicative factor for all color channels (in xyz or rgb spaces). Applied in linear color space, i.e. if the image is gamma corrected the gamma correction is reversed first.

Details

This function writes the image data in img to the file file. Color depth depth is used for writing, but if image has a component "depth", this argument will be ignored. Note: Not all target formats support 16bit coding. The target format is determined from the file extension, and should be one of the many that ImageMagick supports.

Note that write image by default applies a gamma correction with gammatype="ITU". This provides a good standard for display on a screen. For printing cspace="Adobe" should be prefered. Images that are intended for further editing should preferably be saved in an image format that allows for 16Bit depth (tiff, png) using either cspace="Adobe", cspace="wGamut" or cspace="kodak" and preferably no gamma correction to prevent from additional loss of information.

Value

Nothing is returned.

Note

ImageMagick has to be installed on the system to write "tif", "tiff", "png", "gif", "jpg" or "jpeg" files.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(io)
## Not run: demo(io)

Write image RAW data as greyvalue png image

Description

Image RAW data is saved as a 16-Bit greyvalue png image. EXIF information contained in the original RAW image is stored as a comment.

Usage

write.raw(img, filename = "tmp.png")
write.raw(img, filename = "tmp.png")

Arguments

`img`	object of class `"adimpro"` containing image RAW data (`img$type=="RAW"`.)
`filename`	Name of the resulting png-image. If filename does not include an extension ".png" the extension ".png" is added.

Details

EXIF information contained in the original RAW image as well as other available information in object img are added as a comment to the resulting png-image. This comment is evaluated when the image is read by functions read.raw or read.image

Value

Nothing is returned.

Note

ImageMagick has to be installed on the system to write "png" files.

Author(s)

Karsten Tabelow [email protected] and Joerg Polzehl [email protected]

Examples

## Not run: demo(raw)
## Not run: demo(raw)

Package 'adimpro'

Help Index

I/O Functions

Description

Usage

Arguments

Details

Value

Note

Author(s)

See Also

Examples

Set parameters for graphical display

Description

Usage

Arguments

Details

Value

Author(s)

Image Processing

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Propagation-Separation approach for smoothing of 2D images

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Smoothing and demosaicing of RAW images

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Create an image by clipping

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Color Space Conversion

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Pixelwise operations on a pair of images

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Create a color image from RAW image data.

Description

Usage

Arguments

Details