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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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 itkInverseDeconvolutionImageFilter_h
#define itkInverseDeconvolutionImageFilter_h
#include "itkFFTConvolutionImageFilter.h"
#include "itkMath.h"
namespace itk
{
/**
* \class InverseDeconvolutionImageFilter
* \brief The direct linear inverse deconvolution filter.
*
* The inverse filter is the most straightforward deconvolution
* method. Considering that convolution of two images in the spatial domain is
* equivalent to multiplying the Fourier transform of the two images,
* the inverse filter consists of inverting the multiplication. In
* other words, this filter computes the following:
* \f[ hat{F}(\omega) =
* \begin{cases}
* G(\omega) / H(\omega) & \text{if $|H(\omega)| \geq \epsilon$} \\
* 0 & \text{otherwise}
* \end{cases}
* \f]
* where \f$\hat{F}(\omega)\f$ is the Fourier transform of the
* estimate produced by this filter, \f$G(\omega)\f$ is the Fourier
* transform of the input blurred image, \f$H(\omega)\f$ is the
* Fourier transform of the blurring kernel, and \f$\epsilon\f$ is a
* constant real non-negative threshold (called
* KernelZeroMagnitudeThreshold in this filter) that determines when
* the magnitude of a complex number is considered zero.
*
* \author Gaetan Lehmann, Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France
* \author Cory Quammen, The University of North Carolina at Chapel Hill
*
* \ingroup ITKDeconvolution
*
*/
template <typename TInputImage,
typename TKernelImage = TInputImage,
typename TOutputImage = TInputImage,
typename TInternalPrecision = double>
class ITK_TEMPLATE_EXPORT InverseDeconvolutionImageFilter
: public FFTConvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(InverseDeconvolutionImageFilter);
using Self = InverseDeconvolutionImageFilter;
using Superclass = FFTConvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(InverseDeconvolutionImageFilter);
/** Dimensionality of input and output data is assumed to be the same. */
static constexpr unsigned int ImageDimension = TInputImage::ImageDimension;
using InputImageType = TInputImage;
using OutputImageType = TOutputImage;
using KernelImageType = TKernelImage;
using typename Superclass::InputPixelType;
using typename Superclass::OutputPixelType;
using typename Superclass::KernelPixelType;
using typename Superclass::InputIndexType;
using typename Superclass::OutputIndexType;
using typename Superclass::KernelIndexType;
using typename Superclass::InputSizeType;
using typename Superclass::OutputSizeType;
using typename Superclass::KernelSizeType;
using typename Superclass::SizeValueType;
using typename Superclass::InputRegionType;
using typename Superclass::OutputRegionType;
using typename Superclass::KernelRegionType;
/** Internal image types. */
using typename Superclass::InternalImageType;
using typename Superclass::InternalImagePointerType;
using typename Superclass::InternalComplexType;
using typename Superclass::InternalComplexImageType;
using typename Superclass::InternalComplexImagePointerType;
/** Set/get the threshold value used to determine whether a
* frequency of the Fourier transform of the blurring kernel is
* considered to be zero. Default value is 1.0e-4. */
itkSetMacro(KernelZeroMagnitudeThreshold, double);
itkGetConstMacro(KernelZeroMagnitudeThreshold, double);
protected:
InverseDeconvolutionImageFilter();
~InverseDeconvolutionImageFilter() override = default;
/** This filter uses a minipipeline to compute the output. */
void
GenerateData() override;
void
PrintSelf(std::ostream & os, Indent indent) const override;
private:
double m_KernelZeroMagnitudeThreshold{};
};
namespace Functor
{
template <typename TInput1, typename TInput2, typename TOutput>
class ITK_TEMPLATE_EXPORT InverseDeconvolutionFunctor
{
public:
InverseDeconvolutionFunctor() { m_KernelZeroMagnitudeThreshold = 0.0; }
~InverseDeconvolutionFunctor() = default;
bool
operator==(const InverseDeconvolutionFunctor &) const
{
return true;
}
ITK_UNEQUAL_OPERATOR_MEMBER_FUNCTION(InverseDeconvolutionFunctor);
inline TOutput
operator()(const TInput1 & I, const TInput2 & H) const
{
const double absH = itk::Math::abs(H);
TOutput value{};
if (absH >= m_KernelZeroMagnitudeThreshold)
{
value = static_cast<TOutput>(I / H);
}
return value;
}
/** Set/get the threshold value below which complex magnitudes are considered
* to be zero. */
void
SetKernelZeroMagnitudeThreshold(double mu)
{
m_KernelZeroMagnitudeThreshold = mu;
}
double
GetKernelZeroMagnitudeThreshold() const
{
return m_KernelZeroMagnitudeThreshold;
}
private:
double m_KernelZeroMagnitudeThreshold;
};
} // namespace Functor
} // namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkInverseDeconvolutionImageFilter.hxx"
#endif
#endif
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