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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkShotNoiseImageFilter_h
#define itkShotNoiseImageFilter_h
#include "itkNoiseBaseImageFilter.h"
namespace itk
{
/** \class ShotNoiseImageFilter
*
* \brief Alter an image with shot noise.
*
* The shot noise follows a Poisson distribution:
*
* \par
* \f$ I = N(I_0) \f$
*
* \par
* where \f$ N(I_0) \f$ is a Poisson-distributed random variable of mean
* \f$ I_0 \f$. The noise is thus dependent on the pixel intensities in the
* image.
*
* The intensities in the image can be scaled by a user provided value to map
* pixel values to the actual number of particles. The scaling can be seen as
* the inverse of the gain used during the acquisition. The noisy signal is
* then scaled back to its input intensity range:
*
* \par
* \f$ I = \frac{N(I_0 \times s)}{s} \f$
*
* \par
* where \f$ s \f$ is the scale factor.
*
* The Poisson-distributed variable \f$ \lambda \f$ is computed by using the
* algorithm:
*
* \par
* \f$ \begin{array}{l}
* k \leftarrow 0 \\
* p \leftarrow 1 \\
* \textbf{repeat} \\
* \left\{
* \begin{array}{l}
* k \leftarrow k+1 \\
* p \leftarrow p \ast U()
* \end{array}
* \right. \\
* \textbf{until } p > e^{\lambda} \\
* \textbf{return} (k)
* \end{array} \f$
*
* \par
* where \f$ U() \f$ provides a uniformly distributed random variable in the
* interval \f$ [0,1] \f$.
*
* This algorithm is very inefficient for large values of \f$ \lambda \f$,
* though. Fortunately, the Poisson distribution can be accurately approximated
* by a Gaussian distribution of mean and variance \f$ \lambda \f$ when
* \f$ \lambda \f$ is large enough. In this implementation, this value is
* considered to be 50. This leads to the faster algorithm:
*
* \par
* \f$ \lambda + \sqrt{\lambda} \times N()\f$
*
* \par
* where \f$ N() \f$ is a normally distributed random variable of mean 0 and variance 1.
*
* \author Gaetan Lehmann
*
* This code was contributed in the Insight Journal paper "Noise
* Simulation". https://hdl.handle.net/10380/3158
*
* \ingroup ITKImageNoise
*/
template <class TInputImage, class TOutputImage=TInputImage>
class ITK_TEMPLATE_EXPORT ShotNoiseImageFilter :
public NoiseBaseImageFilter<TInputImage,TOutputImage >
{
public:
/** Standard class typedefs. */
typedef ShotNoiseImageFilter Self;
typedef NoiseBaseImageFilter< TInputImage,TOutputImage > Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Run-time type information (and related methods). */
itkTypeMacro(ShotNoiseImageFilter, NoiseBaseImageFilter);
/** Superclass typedefs. */
typedef typename Superclass::OutputImageType OutputImageType;
typedef typename Superclass::OutputImagePointer OutputImagePointer;
typedef typename Superclass::OutputImageRegionType OutputImageRegionType;
typedef typename Superclass::OutputImagePixelType OutputImagePixelType;
/** Some convenient typedefs. */
typedef TInputImage InputImageType;
typedef typename InputImageType::Pointer InputImagePointer;
typedef typename InputImageType::ConstPointer InputImageConstPointer;
typedef typename InputImageType::RegionType InputImageRegionType;
typedef typename InputImageType::PixelType InputImagePixelType;
/** Set/Get the value to map the pixel value to the actual particle counting.
* The scaling can be seen as the inverse of the gain used during the
* acquisition. The noisy signal is then scaled back to its input intensity
* range. Defaults to 1.0. */
itkGetConstMacro(Scale, double);
itkSetMacro(Scale, double);
#ifdef ITK_USE_CONCEPT_CHECKING
/** Begin concept checking */
itkConceptMacro(InputConvertibleToOutputCheck,
(Concept::Convertible<typename TInputImage::PixelType,
typename TOutputImage::PixelType>) );
/** End concept checking */
#endif
protected:
ShotNoiseImageFilter();
virtual ~ShotNoiseImageFilter() ITK_OVERRIDE {}
void PrintSelf(std::ostream & os, Indent indent) const ITK_OVERRIDE;
void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, ThreadIdType threadId ) ITK_OVERRIDE;
private:
ITK_DISALLOW_COPY_AND_ASSIGN(ShotNoiseImageFilter);
double m_Scale;
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkShotNoiseImageFilter.hxx"
#endif
#endif
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