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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkBilateralImageFilter.h,v $
Language: C++
Date: $Date: 2006-03-24 16:03:16 $
Version: $Revision: 1.19 $
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 __itkBilateralImageFilter_h
#define __itkBilateralImageFilter_h
#include "itkImageToImageFilter.h"
#include "itkImage.h"
#include "itkFixedArray.h"
#include "itkNeighborhoodIterator.h"
#include "itkConstNeighborhoodIterator.h"
#include "itkNeighborhood.h"
namespace itk
{
/**
* \class BilateralImageFilter
* \brief Blurs an image while preserving edges
*
* This filter uses bilateral filtering to blur an image using both
* domain and range "neighborhoods". Pixels that are close to a pixel
* in the image domain and similar to a pixel in the image range are
* used to calculate the filtered value. Two gaussian kernels (one in
* the image domain and one in the image range) are used to smooth
* the image. The result is an image that is smoothed in homogeneous
* regions yet has edges preserved. The result is similar to
* anisotropic diffusion but the implementation in non-iterative.
* Another benefit to bilateral filtering is that any distance metric
* can be used for kernel smoothing the image range. Hence, color
* images can be smoothed as vector images, using the CIE distances
* between intensity values as the similarity metric (the Gaussian
* kernel for the image domain is evaluated using CIE distances).
* A separate version of this filter will be designed for color
* and vector images.
*
* Bilateral filtering is capable of reducing the noise in an image
* by an order of magnitude while maintaining edges.
*
* The bilateral operator used here was described by Tomasi and
* Manduchi (Bilateral Filtering for Gray and ColorImages. IEEE
* ICCV. 1998.)
*
* \sa GaussianOperator
* \sa AnisotropicDiffusionImageFilter
* \sa Image
* \sa Neighborhood
* \sa NeighborhoodOperator
*
* \ingroup ImageEnhancement
* \ingroup ImageFeatureExtraction
* \todo Support color images
* \todo Support vector images
*/
template <class TInputImage, class TOutputImage >
class ITK_EXPORT BilateralImageFilter :
public ImageToImageFilter< TInputImage, TOutputImage >
{
public:
/** Standard class typedefs. */
typedef BilateralImageFilter Self;
typedef ImageToImageFilter< 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(BilateralImageFilter, ImageToImageFilter);
/** Image type information. */
typedef TInputImage InputImageType;
typedef TOutputImage OutputImageType;
/** Superclass typedefs. */
typedef typename Superclass::OutputImageRegionType OutputImageRegionType;
/** Extract some information from the image types. Dimensionality
* of the two images is assumed to be the same. */
typedef typename TOutputImage::PixelType OutputPixelType;
typedef typename TOutputImage::InternalPixelType OutputInternalPixelType;
typedef typename NumericTraits<OutputPixelType>::RealType OutputPixelRealType;
typedef typename TInputImage::PixelType InputPixelType;
typedef typename TInputImage::InternalPixelType InputInternalPixelType;
/** Extract some information from the image types. Dimensionality
* of the two images is assumed to be the same. */
itkStaticConstMacro(ImageDimension, unsigned int,
TOutputImage::ImageDimension);
/** Typedef of double containers */
typedef FixedArray<double, itkGetStaticConstMacro(ImageDimension)> ArrayType;
/** Neighborhood iterator types. */
typedef ConstNeighborhoodIterator<TInputImage>
NeighborhoodIteratorType ;
/** Kernel typedef. */
typedef
Neighborhood<double, itkGetStaticConstMacro(ImageDimension)> KernelType;
typedef typename KernelType::SizeType SizeType;
/** Kernel iterator. */
typedef typename KernelType::Iterator KernelIteratorType ;
typedef typename KernelType::ConstIterator KernelConstIteratorType ;
/** Gaussian image type */
typedef
Image<double, itkGetStaticConstMacro(ImageDimension)> GaussianImageType;
/** Standard get/set macros for filter parameters.
* DomainSigma is specified in the same units as the Image spacing.
* RangeSigma is specified in the units of intensity. */
itkSetMacro(DomainSigma, ArrayType);
itkGetMacro(DomainSigma, const ArrayType);
itkSetMacro(RangeSigma, double);
itkGetMacro(RangeSigma, double);
itkGetMacro(FilterDimensionality, unsigned int);
itkSetMacro(FilterDimensionality, unsigned int);
/** Convenience get/set methods for setting all domain parameters to the
* same values. */
void SetDomainSigma(const double v)
{
m_DomainSigma.Fill(v);
}
/** Control automatic kernel size determination. When
* automatic is "on", the kernel size is a function of the domain
* sigma. When automatic is "off", the kernel size is whatever is
* specified by the user.
* \sa SetRadius() */
itkBooleanMacro(AutomaticKernelSize);
itkGetMacro(AutomaticKernelSize, bool);
itkSetMacro(AutomaticKernelSize, bool);
/** Set/Get the kernel radius, specified in pixels. This parameter
* is used only when AutomaticNeighborhoodSize is "off". */
void SetRadius(const unsigned long);
itkSetMacro(Radius, SizeType);
itkGetConstReferenceMacro(Radius, SizeType);
/** Set/Get the number of samples in the approximation to the Gaussian
* used for the range smoothing. Samples are only generated in the
* range of [0, 4*m_RangeSigma]. Default is 100. */
itkSetMacro(NumberOfRangeGaussianSamples, unsigned long);
itkGetMacro(NumberOfRangeGaussianSamples, unsigned long);
#ifdef ITK_USE_CONCEPT_CHECKING
/** Begin concept checking */
itkConceptMacro(OutputHasNumericTraitsCheck,
(Concept::HasNumericTraits<OutputPixelType>));
/** End concept checking */
#endif
protected:
/** Constructor. Default value for DomainSigma is 4. Default value
* RangeSigma is 50. */
BilateralImageFilter()
{
m_Radius.Fill(1);
m_AutomaticKernelSize = true;
m_DomainSigma.Fill(4.0);
m_RangeSigma = 50.0;
m_FilterDimensionality = ImageDimension;
m_NumberOfRangeGaussianSamples = 100;
m_DynamicRange = 0.0;
m_DynamicRangeUsed = 0.0;
m_DomainMu = 2.5; // keep small to keep kernels small
m_RangeMu = 4.0; // can be bigger then DomainMu since we only
// index into a single table
}
virtual ~BilateralImageFilter() {}
void PrintSelf(std::ostream& os, Indent indent) const;
/** Do some setup before the ThreadedGenerateData */
void BeforeThreadedGenerateData();
/** Standard pipeline method. This filter is implemented as a multi-threaded
* filter. */
void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread,
int threadId);
/** BilateralImageFilter needs a larger input requested region than
* the output requested region (larger by the size of the domain
* Gaussian kernel). As such, BilateralImageFilter needs to provide
* an implementation for GenerateInputRequestedRegion() in order to
* inform the pipeline execution model.
* \sa ImageToImageFilter::GenerateInputRequestedRegion() */
virtual void GenerateInputRequestedRegion() throw(InvalidRequestedRegionError);
private:
BilateralImageFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
/** The standard deviation of the gaussian blurring kernel in the image
range. Units are intensity. */
double m_RangeSigma;
/** The standard deviation of the gaussian blurring kernel in each
dimensional direction. Units match image spacing units. */
ArrayType m_DomainSigma;
/** Multiplier used to define statistical thresholds. Gaussians are
* only evaluated to m_DomainMu*m_DomainSigma or m_RangeMu*m_RangeSigma. */
double m_DomainMu;
double m_RangeMu;
/** Number of dimensions to process. Default is all dimensions */
unsigned int m_FilterDimensionality;
/** Gaussian kernel used for smoothing in the spatial domain */
KernelType m_GaussianKernel;
SizeType m_Radius;
bool m_AutomaticKernelSize;
/** Variables for the lookup table of range gaussian values */
unsigned long m_NumberOfRangeGaussianSamples;
double m_DynamicRange;
double m_DynamicRangeUsed;
std::vector<double> m_RangeGaussianTable;
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkBilateralImageFilter.txx"
#endif
#endif
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