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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkRecursiveGaussianImageFilter.h,v $
Language: C++
Date: $Date: 2005-05-12 14:42:04 $
Version: $Revision: 1.26 $
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef __itkRecursiveGaussianImageFilter_h
#define __itkRecursiveGaussianImageFilter_h
#include "itkRecursiveSeparableImageFilter.h"
namespace itk
{
/** \class RecursiveGaussianImageFilter
* \brief Base class for computing IIR convolution with an approximation of a Gaussian kernel.
*
* \f[
* \frac{ 1 }{ \sigma \sqrt{ 2 \pi } } \exp{ \left( - \frac{x^2}{ 2 \sigma^2 } \right) }
* \f]
*
* RecursiveGaussianImageFilter is the base class for recursive filters that
* approximate convolution with the Gaussian kernel.
* This class implements the recursive filtering
* method proposed by R.Deriche in IEEE-PAMI
* Vol.12, No.1, January 1990, pp 78-87,
* "Fast Algorithms for Low-Level Vision"
*
* Details of the implementation are described in the technical report:
* R. Deriche, "Recursively Implementing The Gaussian and Its Derivatives",
* INRIA, 1993, ftp://ftp.inria.fr/INRIA/tech-reports/RR/RR-1893.ps.gz
*
* Further improvements of the algorithm are described in:
* G. Farneback & C.-F. Westin, "On Implementation of Recursive Gaussian
* Filters", so far unpublished.
*
* As compared to itk::DiscreteGaussianImageFilter, this filter tends
* to be faster for large kernels, and it can take the derivative
* of the blurred image in one step. Also, note that we have
* itk::RecursiveGaussianImageFilter::SetSigma(), but
* itk::DiscreteGaussianImageFilter::SetVariance().
*
* \ingroup ImageEnhancement Singlethreaded
*/
template <typename TInputImage, typename TOutputImage=TInputImage>
class ITK_EXPORT RecursiveGaussianImageFilter :
public RecursiveSeparableImageFilter<TInputImage,TOutputImage>
{
public:
/** Standard class typedefs. */
typedef RecursiveGaussianImageFilter Self;
typedef RecursiveSeparableImageFilter<TInputImage,TOutputImage> Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
typedef typename Superclass::RealType RealType;
typedef typename Superclass::ScalarRealType ScalarRealType;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Type macro that defines a name for this class */
itkTypeMacro( RecursiveGaussianImageFilter, RecursiveSeparableImageFilter );
/** Set/Get the Sigma, measured in world coordinates, of the Gaussian
* kernel. The default is 1.0. */
itkGetMacro( Sigma, ScalarRealType );
itkSetMacro( Sigma, ScalarRealType );
/** Enum type that indicates if the filter applies the equivalent operation
of convolving with a gaussian, first derivative of a gaussian or the
second derivative of a gaussian. */
typedef enum { ZeroOrder, FirstOrder, SecondOrder } OrderEnumType;
/** Type of the output image */
typedef TOutputImage OutputImageType;
/** Set/Get the flag for normalizing the gaussian over scale space.
When this flag is ON the filter will be normalized in such a way
that larger sigmas will not result in the image fading away.
\f[
\frac{ 1 }{ \sqrt{ 2 \pi } };
\f]
When the flag is OFF the normalization will conserve contant the
integral of the image intensity.
\f[
\frac{ 1 }{ \sigma \sqrt{ 2 \pi } };
\f]
For analyzing an image across Scale Space you want to enable
this flag. It is disabled by default. */
itkSetMacro( NormalizeAcrossScale, bool );
itkGetMacro( NormalizeAcrossScale, bool );
/** Set/Get the Order of the Gaussian to convolve with.
\li ZeroOrder is equivalent to convolving with a Gaussian. This
is the default.
\li FirstOrder is equivalent to convolving with the first derivative of a Gaussian.
\li SecondOrder is equivalent to convolving with the second derivative of a Gaussian.
*/
itkSetMacro( Order, OrderEnumType );
itkGetMacro( Order, OrderEnumType );
/** Explicitly set a zeroth order derivative. */
void SetZeroOrder();
/** Explicitly set a first order derivative. */
void SetFirstOrder();
/** Explicitly set a second order derivative. */
void SetSecondOrder();
protected:
RecursiveGaussianImageFilter();
virtual ~RecursiveGaussianImageFilter() {};
void PrintSelf(std::ostream& os, Indent indent) const;
/** Set up the coefficients of the filter to approximate a specific kernel.
* Here it is used to approximate a Gaussian or one of its
* derivatives. Parameter is the spacing along the dimension to
* filter. */
virtual void SetUp(ScalarRealType spacing);
private:
RecursiveGaussianImageFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
/** Compute the N coefficients in the recursive filter. */
void ComputeNCoefficients(ScalarRealType sigmad,
ScalarRealType A1, ScalarRealType B1, ScalarRealType W1, ScalarRealType L1,
ScalarRealType A2, ScalarRealType B2, ScalarRealType W2, ScalarRealType L2,
ScalarRealType& N0, ScalarRealType& N1,
ScalarRealType& N2, ScalarRealType& N3,
ScalarRealType& SN, ScalarRealType& DN, ScalarRealType& EN);
/** Compute the D coefficients in the recursive filter. */
void ComputeDCoefficients(ScalarRealType sigmad,
ScalarRealType W1, ScalarRealType L1, ScalarRealType W2, ScalarRealType L2,
ScalarRealType& SD, ScalarRealType& DD, ScalarRealType& ED);
/** Compute the M coefficients and the boundary coefficients in the
* recursive filter. */
void ComputeRemainingCoefficients(bool symmetric);
/** Sigma of the gaussian kernel. */
ScalarRealType m_Sigma;
/** Normalize the image across scale space */
bool m_NormalizeAcrossScale;
OrderEnumType m_Order;
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkRecursiveGaussianImageFilter.txx"
#endif
#endif
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