The function integrates smoothing and demosaicing of RAW image data.
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)Object of class "adimpro"
Contains the reconstructed image.
Contains the sum of weights, i.e. trace(W_i), in all grid points i.
Contains the maximum sum of weights for an nonadaptive kernel estimate with the same bandwidth.
Bandwidth used in the last iteration.
The arguments of the function call.
Estimated coefficients in the linear variance model for the color channels.
an object of class adimpro containing the RAW image data.
See read.raw for creating such objects.
maximal bandwidth to use in the smoothing algorithm.
use adaptive weights if aws==TRUE.
Vector containing factors for the three color chanels, allows to change the white balance.
Color space of the result,
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.
If TRUE increase range of values to maximum.
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.
(logical). If graph=TRUE intermediate results are
illustrated after each iteration step. Defaults to FALSE.
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.
logical, determines if image data are stored in raw-format.
Karsten Tabelow tabelow@wias-berlin.de and Joerg Polzehl polzehl@wias-berlin.de
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.
Polzehl, J. and Tabelow, K. (2007). Adaptive smoothing of digital images, Journal of Statistical Software 19 (1).
read.raw,awsimage, make.image, show.image, clip.image