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Noise Normalization |
Classes | |
class | NoiseNormalizationOptions |
Pass options to one of the noise normalization functions. More... | |
Functions | |
template<... > | |
bool | linearNoiseNormalization (...) |
Noise normalization by means of an estimated or given linear noise model. More... | |
template<... > | |
void | noiseVarianceClustering (...) |
Determine the noise variance as a function of the image intensity and cluster the results. More... | |
template<... > | |
void | noiseVarianceEstimation (...) |
Determine the noise variance as a function of the image intensity. More... | |
template<... > | |
bool | nonparametricNoiseNormalization (...) |
Noise normalization by means of an estimated non-parametric noise model. More... | |
template<... > | |
bool | quadraticNoiseNormalization (...) |
Noise normalization by means of an estimated or given quadratic noise model. More... | |
Estimate noise with intensity-dependent variance and transform it into additive Gaussian noise.
void vigra::noiseVarianceEstimation | ( | ... | ) |
Determine the noise variance as a function of the image intensity.
This operator applies an algorithm described in
W. Förstner: "Image Preprocessing for Feature Extraction in Digital Intensity, Color and Range Images", Proc. Summer School on Data Analysis and the Statistical Foundations of Geomatics, Lecture Notes in Earth Science, Berlin: Springer, 1999
in order to estimate the noise variance as a function of the image intensity in a robust way, i.e. so that intensity changes due to edges do not bias the estimate. The source value type (SrcAccessor::value_type
) must be a scalar type which is convertible to double
. The result is written into the result sequence, whose value_type
must be constructible from two double
values. The following options can be set via the options object (see vigra::NoiseNormalizationOptions for details):
useGradient
, windowRadius
, noiseEstimationQuantile
, noiseVarianceInitialGuess
Declarations:
pass 2D array views:
Usage:
#include <vigra/noise_normalization.hxx>
Namespace: vigra
void vigra::noiseVarianceClustering | ( | ... | ) |
Determine the noise variance as a function of the image intensity and cluster the results.
This operator first calls noiseVarianceEstimation() to obtain a sequence of intensity/variance pairs, which are then clustered using the median cut algorithm. Then the cluster centers (i.e. average variance vs. average intensity) are determined and returned in the result sequence.
In addition to the options valid for noiseVarianceEstimation(), the following options can be set via the options object (see vigra::NoiseNormalizationOptions for details):
clusterCount
, averagingQuantile
Declarations:
pass 2D array views:
Usage:
#include <vigra/noise_normalization.hxx>
Namespace: vigra
bool vigra::nonparametricNoiseNormalization | ( | ... | ) |
Noise normalization by means of an estimated non-parametric noise model.
The original image is assumed to be corrupted by noise whose variance depends on the intensity in an unknown way. The present functions first calls noiseVarianceClustering() to obtain a sequence of intensity/variance pairs (cluster centers) which estimate this dependency. The cluster centers are connected into a piecewise linear function which is the inverted according to the formula derived in
W. Förstner: "Image Preprocessing for Feature Extraction in Digital Intensity, Color and Range Images", Proc. Summer School on Data Analysis and the Statistical Foundations of Geomatics, Lecture Notes in Earth Science, Berlin: Springer, 1999
The inverted formula defines a pixel-wise intensity transformation whose application turns the original image into one that is corrupted by additive Gaussian noise with unit variance. Most subsequent algorithms will be able to handle this type of noise much better than the original noise.
RGB and other multiband images will be processed one band at a time. The function returns true
on success. Noise normalization will fail if the original image does not contain sufficiently homogeneous regions that allow robust estimation of the noise variance.
The options object may use all options described in vigra::NoiseNormalizationOptions.
The function returns false
if the noise estimation failed, so that no normalization could be performed.
Declarations:
pass 2D array views:
Usage:
#include <vigra/noise_normalization.hxx>
Namespace: vigra
bool vigra::quadraticNoiseNormalization | ( | ... | ) |
Noise normalization by means of an estimated or given quadratic noise model.
This function works like nonparametricNoiseNormalization() excapt that the model for the dependency between intensity and noise variance is assumed to be a quadratic function rather than a piecewise linear function. If the data conform to the quadratic model, this leads to a somewhat smoother transformation. The function returns false
if the noise estimation failed, so that no normalization could be performed.
In the second variant of the function, the parameters of the quadratic model are not estimated, but explicitly given according to:
Declarations:
pass 2D array views:
Usage:
#include <vigra/noise_normalization.hxx>
Namespace: vigra
Required Interface:
The source value type must be convertible to double
or must be a vector whose elements are convertible to double
. Likewise, the destination type must be assignable from double
or a vector whose elements are assignable from double
.
bool vigra::linearNoiseNormalization | ( | ... | ) |
Noise normalization by means of an estimated or given linear noise model.
This function works like nonparametricNoiseNormalization() excapt that the model for the dependency between intensity and noise variance is assumed to be a linear function rather than a piecewise linear function. If the data conform to the linear model, this leads to a very simple transformation which is similar to the familiar gamma correction. The function returns false
if the noise estimation failed, so that no normalization could be performed.
In the second variant of the function, the parameters of the linear model are not estimated, but explicitly given according to:
Declarations:
pass 2D array views:
Usage:
#include <vigra/noise_normalization.hxx>
Namespace: vigra
Required Interface:
The source value type must be convertible to double
or must be a vector whose elements are convertible to double
. Likewise, the destination type must be assignable from double
or a vector whose elements are assignable from double
.
© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) |
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