itkPatchBasedDenoisingImageFilter.h

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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
 *
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 *  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 itkPatchBasedDenoisingImageFilter_h
#define itkPatchBasedDenoisingImageFilter_h

#include "itkPatchBasedDenoisingBaseImageFilter.h"
#include "itkImageRegionIterator.h"
#include "itkNeighborhoodAlgorithm.h"
#include "itkVector.h"
#include "itkVectorImage.h"
#include "itkRGBPixel.h"
#include "itkRGBAPixel.h"
#include "itkDiffusionTensor3D.h"
#include "itkFixedArray.h"
#include "itkMatrix.h"
#include "itkRegionConstrainedSubsampler.h"
#include "itkEnableIf.h"
#include "itkIsSame.h"

#include <vector>

namespace itk
{
template <typename TInputImage, typename TOutputImage>
class ITK_TEMPLATE_EXPORT PatchBasedDenoisingImageFilter :
  public PatchBasedDenoisingBaseImageFilter<TInputImage, TOutputImage>
{
public:
  typedef PatchBasedDenoisingImageFilter                                Self;
  typedef PatchBasedDenoisingBaseImageFilter<TInputImage, TOutputImage> Superclass;
  typedef SmartPointer<Self>                                            Pointer;
  typedef SmartPointer<const Self>                                      ConstPointer;
  typedef typename Superclass::OutputImagePointer                       OutputImagePointer;

  itkNewMacro(Self);

  itkTypeMacro(PatchBasedDenoisingImageFilter, ImageToImageFilter);

  typedef typename Superclass::InputImageType  InputImageType;
  typedef typename Superclass::OutputImageType OutputImageType;

  itkStaticConstMacro(ImageDimension, unsigned int,
                      Superclass::ImageDimension);

  typedef typename InputImageType::RegionType InputImageRegionType;

  typedef ImageRegionIterator<OutputImageType>     OutputImageRegionIteratorType;
  typedef ImageRegionConstIterator<InputImageType> InputImageRegionConstIteratorType;

  typedef typename Superclass::PixelType      PixelType;
  typedef typename Superclass::PixelValueType PixelValueType;

  typedef typename NumericTraits< PixelType >::RealType      RealType;
  typedef typename NumericTraits< PixelValueType >::RealType RealValueType;
  typedef Array<PixelValueType>                              PixelArrayType;
  typedef Array<RealValueType>                               RealArrayType;
  typedef Array<unsigned short>                              ShortArrayType;

  typedef typename Superclass::ListAdaptorType         ListAdaptorType;
  typedef typename Superclass::PatchRadiusType         PatchRadiusType;
  typedef typename Superclass::InputImagePatchIterator InputImagePatchIterator;
  typedef ListAdaptorType                              PatchSampleType;
  typedef typename Superclass::PatchWeightsType        PatchWeightsType;

  typedef itk::Statistics::RegionConstrainedSubsampler<
      PatchSampleType, InputImageRegionType >          BaseSamplerType;
  typedef typename BaseSamplerType::Pointer            BaseSamplerPointer;
  typedef typename BaseSamplerType::InstanceIdentifier InstanceIdentifier;

  typedef FixedArray< PixelValueType, 3 >      EigenValuesArrayType;
  typedef Matrix< PixelValueType, 3, 3 >       EigenVectorsMatrixType;
  typedef std::vector<EigenValuesArrayType>    EigenValuesCacheType;
  typedef std::vector<EigenVectorsMatrixType>  EigenVectorsCacheType;

  struct ThreadDataStruct
    {
    ShortArrayType validDerivatives;
    RealArrayType entropyFirstDerivative;
    RealArrayType entropySecondDerivative;
    ShortArrayType validNorms;
    RealArrayType minNorm;
    RealArrayType maxNorm;
    BaseSamplerPointer sampler;
    EigenValuesCacheType eigenValsCache;
    EigenVectorsCacheType eigenVecsCache;
    };

  itkSetMacro(UseSmoothDiscPatchWeights, bool);
  itkBooleanMacro(UseSmoothDiscPatchWeights);
  itkGetConstMacro(UseSmoothDiscPatchWeights, bool);

  void SetKernelBandwidthSigma(const RealArrayType& kernelSigma);
  itkGetConstMacro(KernelBandwidthSigma, RealArrayType);

  itkSetClampMacro(KernelBandwidthFractionPixelsForEstimation, double, 0.01, 1.0);
  itkGetConstReferenceMacro(KernelBandwidthFractionPixelsForEstimation, double);

  itkSetMacro(ComputeConditionalDerivatives, bool);
  itkBooleanMacro(ComputeConditionalDerivatives);
  itkGetConstMacro(ComputeConditionalDerivatives, bool);

  itkSetMacro(UseFastTensorComputations, bool);
  itkBooleanMacro(UseFastTensorComputations);
  itkGetConstMacro(UseFastTensorComputations, bool);

  itkStaticConstMacro(MaxSigmaUpdateIterations, unsigned int,
                      20);

  itkSetClampMacro(KernelBandwidthMultiplicationFactor, double, 0.01, 100);
  itkGetConstReferenceMacro(KernelBandwidthMultiplicationFactor, double);

  void SetNoiseSigma(const RealType& sigma);

  itkGetConstMacro(NoiseSigma, RealType);

  itkSetObjectMacro(Sampler, BaseSamplerType);
  itkGetModifiableObjectMacro(Sampler, BaseSamplerType);

protected:
  PatchBasedDenoisingImageFilter();
  ~PatchBasedDenoisingImageFilter();
  virtual void PrintSelf(std::ostream& os, Indent indent) const ITK_OVERRIDE;

  virtual void EmptyCaches();

  virtual void AllocateUpdateBuffer() ITK_OVERRIDE;

  virtual void CopyInputToOutput() ITK_OVERRIDE;

  virtual void GenerateInputRequestedRegion() ITK_OVERRIDE;

  template< typename T>
  typename EnableIfC<
    IsSame<T, typename NumericTraits<T>::ValueType>::Value,
    T >::Type
  GetComponent(const T pix,
               unsigned int itkNotUsed( idx ) ) const
  {
    // The enable if idiom is used to overload this method for both
    // scalars and multi-component types. By exploiting that
    // NumericTraits' ValueType typedef (defines the per-element type
    // for multi-component types ) is different then the parameterize
    // type, the bracket operator is used only for multi-component
    // types.
    return pix;
  }

  template< typename T>
  typename DisableIfC<
    IsSame<T, typename NumericTraits<T>::ValueType>::Value,
    typename NumericTraits<T>::ValueType>::Type
  GetComponent(const T& pix,
               unsigned int idx ) const
  {
    return pix[idx];
  }

  template< typename T >
  void
  SetComponent( T &pix,
                unsigned int itkNotUsed( idx ),
                typename EnableIfC< IsSame<T,
                                           typename NumericTraits<T>::ValueType>::Value, RealValueType>::Type val) const
  {
    pix = val;
  }

  template< typename T >
  void
  SetComponent( T &pix,
                unsigned int idx,
                typename DisableIfC< IsSame<T,
                                            typename NumericTraits<T>::ValueType>::Value,
                                     RealValueType>::Type val) const
  {
    pix[idx] =  val;
  }

  void ComputeMinMax(const Image< DiffusionTensor3D<PixelValueType> , ImageDimension>* img)
  {
    if (this->m_ComponentSpace == Superclass::RIEMANNIAN)
      {
      DispatchedRiemannianMinMax(img);
      }
    else
      {
      DispatchedArrayMinMax(img);
      }
  }

  template< typename TImageType>
  typename EnableIfC<
    IsSame<typename TImageType::PixelType, typename NumericTraits<typename TImageType::PixelType>::ValueType>::Value
    >::Type
  ComputeMinMax(const TImageType* img)
  {
    DispatchedMinMax(img);
  }

  template< typename TImageType>
  typename DisableIfC<
    IsSame<typename TImageType::PixelType, typename NumericTraits<typename TImageType::PixelType>::ValueType>::Value
    >::Type
  ComputeMinMax(const TImageType* img)
  {
    DispatchedArrayMinMax(img);
  }

  void ComputeDifferenceAndWeightedSquaredNorm(const DiffusionTensor3D<PixelValueType>& a,
                                               const DiffusionTensor3D<PixelValueType>& b,
                                               const RealArrayType& weight,
                                               bool useCachedComputations,
                                               SizeValueType cacheIndex,
                                               EigenValuesCacheType& eigenValsCache,
                                               EigenVectorsCacheType& eigenVecsCache,
                                               RealType& diff, RealArrayType& norm)
  {
    if (this->m_ComponentSpace == Superclass::RIEMANNIAN)
      {
      ComputeLogMapAndWeightedSquaredGeodesicDifference(a, b, weight,
                                                        useCachedComputations, cacheIndex,
                                                        eigenValsCache, eigenVecsCache,
                                                        diff, norm);
      }
    else
      {
      ComputeSignedEuclideanDifferenceAndWeightedSquaredNorm(a, b, weight,
                                                             useCachedComputations, cacheIndex,
                                                             eigenValsCache, eigenVecsCache,
                                                             diff, norm);
      }
  }

  template <typename PixelT>
  void ComputeDifferenceAndWeightedSquaredNorm(const PixelT& a,
                                               const PixelT& b,
                                               const RealArrayType& weight,
                                               bool useCachedComputations,
                                               SizeValueType cacheIndex,
                                               EigenValuesCacheType& eigenValsCache,
                                               EigenVectorsCacheType& eigenVecsCache,
                                               RealType& diff, RealArrayType& norm)
  {
    ComputeSignedEuclideanDifferenceAndWeightedSquaredNorm(a, b, weight,
                                                           useCachedComputations, cacheIndex,
                                                           eigenValsCache, eigenVecsCache,
                                                           diff, norm);
  }

  RealType AddUpdate(const DiffusionTensor3D<RealValueType>& a,
                     const RealType& b)
  {
    if (this->m_ComponentSpace == Superclass::RIEMANNIAN)
      {
      return this->AddExponentialMapUpdate(a, b);
      }
    else
      {
      return this->AddEuclideanUpdate(a, b);
      }
  }

  template <typename RealT>
  RealType AddUpdate(const RealT& a,
                     const RealType& b)
  {
    return this->AddEuclideanUpdate(a, b);
  }

  virtual void EnforceConstraints();

  virtual void Initialize() ITK_OVERRIDE;

  virtual void InitializeKernelSigma();

  virtual void InitializePatchWeights() ITK_OVERRIDE;

  virtual void InitializePatchWeightsSmoothDisc();

  virtual void InitializeIteration() ITK_OVERRIDE;

  virtual void ComputeKernelBandwidthUpdate() ITK_OVERRIDE; // derived from base class;

  // define here

  virtual ThreadDataStruct ThreadedComputeSigmaUpdate(const InputImageRegionType& regionToProcess,
                                                      const int itkNotUsed(threadId),
                                                      ThreadDataStruct threadData);

  virtual RealArrayType ResolveSigmaUpdate();

  virtual void ComputeImageUpdate() ITK_OVERRIDE;

  virtual ThreadDataStruct ThreadedComputeImageUpdate(const InputImageRegionType& regionToProcess,
                                                      const int threadId,
                                                      ThreadDataStruct threadData);

  virtual RealType ComputeGradientJointEntropy(InstanceIdentifier id,
                                               typename ListAdaptorType::Pointer& inList,
                                               BaseSamplerPointer& sampler,
                                               ThreadDataStruct& threadData);

  virtual void ApplyUpdate() ITK_OVERRIDE;

  virtual void ThreadedApplyUpdate(const InputImageRegionType& regionToProcess,
                                   const int itkNotUsed(threadId) );

  virtual void PostProcessOutput() ITK_OVERRIDE;

  virtual void SetThreadData(int threadId, const ThreadDataStruct& data);

  virtual ThreadDataStruct GetThreadData(int threadId);

  std::vector<ThreadDataStruct> m_ThreadData;

  typename OutputImageType::Pointer m_UpdateBuffer;

  unsigned int m_NumPixelComponents;
  unsigned int m_NumIndependentComponents;
  unsigned int m_TotalNumberPixels;
  //
  bool m_UseSmoothDiscPatchWeights;
  //
  bool m_UseFastTensorComputations;
  //
  RealArrayType  m_KernelBandwidthSigma;
  bool           m_KernelBandwidthSigmaIsSet;
  RealArrayType  m_IntensityRescaleInvFactor;
  PixelType      m_ZeroPixel;
  PixelArrayType m_ImageMin;
  PixelArrayType m_ImageMax;
  double         m_KernelBandwidthFractionPixelsForEstimation;
  bool           m_ComputeConditionalDerivatives;
  double         m_MinSigma;
  double         m_MinProbability;
  unsigned int   m_SigmaUpdateDecimationFactor;
  double         m_SigmaUpdateConvergenceTolerance;
  ShortArrayType m_SigmaConverged;
  double         m_KernelBandwidthMultiplicationFactor;
  //
  RealType m_NoiseSigma;
  RealType m_NoiseSigmaSquared;
  bool     m_NoiseSigmaIsSet;
  //
  BaseSamplerPointer                m_Sampler;
  typename ListAdaptorType::Pointer m_SearchSpaceList;

private:
  ITK_DISALLOW_COPY_AND_ASSIGN(PatchBasedDenoisingImageFilter);

  static ITK_THREAD_RETURN_TYPE ComputeSigmaUpdateThreaderCallback( void *arg );

  static ITK_THREAD_RETURN_TYPE ComputeImageUpdateThreaderCallback( void *arg );

  static ITK_THREAD_RETURN_TYPE ApplyUpdateThreaderCallback( void *arg );

  template <typename TInputImageType>
  void DispatchedMinMax(const TInputImageType* img);

  template <typename TInputImageType>
  void DispatchedArrayMinMax(const TInputImageType* img);

  template <typename TInputImageType>
  void DispatchedVectorMinMax(const TInputImageType* img);

  template <typename TInputImageType>
  void DispatchedRiemannianMinMax(const TInputImageType* img);

  static ITK_THREAD_RETURN_TYPE RiemannianMinMaxThreaderCallback( void *arg );

  ThreadDataStruct ThreadedRiemannianMinMax(const InputImageRegionType& regionToProcess,
                                            const int itkNotUsed(threadId),
                                            const InputImageType* img,
                                            ThreadDataStruct threadData);

  virtual void ResolveRiemannianMinMax();

  void ComputeSignedEuclideanDifferenceAndWeightedSquaredNorm(const PixelType& a, const PixelType& b,
                                                              const RealArrayType& weight,
                                                              bool useCachedComputations,
                                                              SizeValueType cacheIndex,
                                                              EigenValuesCacheType& eigenValsCache,
                                                              EigenVectorsCacheType& eigenVecsCache,
                                                              RealType& diff, RealArrayType& norm);

  void ComputeLogMapAndWeightedSquaredGeodesicDifference(const DiffusionTensor3D<PixelValueType>& spdMatrixA,
                                                         const DiffusionTensor3D<PixelValueType>& spdMatrixB,
                                                         const RealArrayType& weight,
                                                         bool useCachedComputations,
                                                         SizeValueType cacheIndex,
                                                         EigenValuesCacheType& eigenValsCache,
                                                         EigenVectorsCacheType& eigenVecsCache,
                                                         RealType& symMatrixLogMap, RealArrayType& geodesicDist);

  template <typename TensorValueT>
  void Compute3x3EigenAnalysis(const DiffusionTensor3D<TensorValueT>& spdMatrix,
                               FixedArray< TensorValueT, 3 >&  eigenVals,
                               Matrix< TensorValueT, 3, 3 >& eigenVecs);

  RealType AddEuclideanUpdate(const RealType& a, const RealType& b);

  RealType AddExponentialMapUpdate(const DiffusionTensor3D<RealValueType>& spdMatrix,
                                   const DiffusionTensor3D<RealValueType>& symMatrix);

  struct ThreadFilterStruct
    {
    PatchBasedDenoisingImageFilter *Filter;
    InputImageType *Img;
    };

};  // end class PatchBasedDenoisingImageFilter

} // end namespace itk

#ifndef ITK_MANUAL_INSTANTIATION
# include "itkPatchBasedDenoisingImageFilter.hxx"
#endif

#endif