itkWienerDeconvolutionImageFilter.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
 *
 *  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 itkWienerDeconvolutionImageFilter_h
#define itkWienerDeconvolutionImageFilter_h

#include "itkInverseDeconvolutionImageFilter.h"

namespace itk
{
template< typename TInputImage, typename TKernelImage = TInputImage, typename TOutputImage = TInputImage, typename TInternalPrecision=double >
class ITK_TEMPLATE_EXPORT WienerDeconvolutionImageFilter :
  public InverseDeconvolutionImageFilter< TInputImage, TKernelImage, TOutputImage, TInternalPrecision >
{
public:
  ITK_DISALLOW_COPY_AND_ASSIGN(WienerDeconvolutionImageFilter);

  using Self = WienerDeconvolutionImageFilter;
  using Superclass = InverseDeconvolutionImageFilter< TInputImage,
                                           TKernelImage,
                                           TOutputImage,
                                           TInternalPrecision >;
  using Pointer = SmartPointer< Self >;
  using ConstPointer = SmartPointer< const Self >;

  itkNewMacro(Self);

  itkTypeMacro(WienerDeconvolutionImageFilter, InverseDeconvolutionImageFilter);

  static constexpr unsigned int ImageDimension = TInputImage::ImageDimension;

  using InputImageType = TInputImage;
  using OutputImageType = TOutputImage;
  using KernelImageType = TKernelImage;
  using InputPixelType = typename Superclass::InputPixelType;
  using OutputPixelType = typename Superclass::OutputPixelType;
  using KernelPixelType = typename Superclass::KernelPixelType;
  using InputIndexType = typename Superclass::InputIndexType;
  using OutputIndexType = typename Superclass::OutputIndexType;
  using KernelIndexType = typename Superclass::KernelIndexType;
  using InputSizeType = typename Superclass::InputSizeType;
  using OutputSizeType = typename Superclass::OutputSizeType;
  using KernelSizeType = typename Superclass::KernelSizeType;
  using SizeValueType = typename Superclass::SizeValueType;
  using InputRegionType = typename Superclass::InputRegionType;
  using OutputRegionType = typename Superclass::OutputRegionType;
  using KernelRegionType = typename Superclass::KernelRegionType;

  using InternalImageType = typename Superclass::InternalImageType;
  using InternalImagePointerType = typename Superclass::InternalImagePointerType;
  using InternalComplexType = typename Superclass::InternalComplexType;
  using InternalComplexImageType = typename Superclass::InternalComplexImageType;
  using InternalComplexImagePointerType = typename Superclass::InternalComplexImagePointerType;

  itkSetMacro(NoiseVariance, double);
  itkGetConstMacro(NoiseVariance, double);

protected:
  WienerDeconvolutionImageFilter();
  ~WienerDeconvolutionImageFilter() override = default;

  void GenerateData() override;

  void PrintSelf( std::ostream & os, Indent indent ) const override;

private:
  double m_NoiseVariance;
};

namespace Functor
{
template< typename TPixel >
class ITK_TEMPLATE_EXPORT WienerDeconvolutionFunctor
{
public:
  WienerDeconvolutionFunctor() { m_KernelZeroMagnitudeThreshold = 0.0; }
  ~WienerDeconvolutionFunctor() = default;

  bool operator!=( const WienerDeconvolutionFunctor & ) const
  {
    return false;
  }
  bool operator==( const WienerDeconvolutionFunctor & other) const
  {
    return !(*this != other);
  }
  inline TPixel operator()(const TPixel & I, const TPixel & H) const
  {
    TPixel Pn = m_NoisePowerSpectralDensityConstant;

    // We estimate the power spectral density of the output image to
    // be the same as the power spectral density of the blurred input
    // minus the power spectral density of the noise.
    TPixel Pf = std::norm( I );

    TPixel denominator = std::norm( H ) + ( Pn / (Pf - Pn) );
    TPixel value = NumericTraits< TPixel >::ZeroValue();
    if ( std::abs( denominator ) >= m_KernelZeroMagnitudeThreshold )
      {
      value = I * ( std::conj( H ) / denominator );
      }

    return value;
  }

  void SetNoisePowerSpectralDensityConstant(double constant)
  {
    m_NoisePowerSpectralDensityConstant = constant;
  }
  double GetNoisePowerSpectralDensityConstant() const
  {
    return m_NoisePowerSpectralDensityConstant;
  }

  void SetKernelZeroMagnitudeThreshold(double mu)
  {
    m_KernelZeroMagnitudeThreshold = mu;
  }
  double GetKernelZeroMagnitudeThreshold() const
  {
    return m_KernelZeroMagnitudeThreshold;
  }

private:
  double m_NoisePowerSpectralDensityConstant;
  double m_KernelZeroMagnitudeThreshold;
};
} //namespace Functor

}

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

#endif