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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 itkLandweberDeconvolutionImageFilter_h
#define itkLandweberDeconvolutionImageFilter_h
#include "itkIterativeDeconvolutionImageFilter.h"
#include "itkComplexConjugateImageAdaptor.h"
#include "itkTernaryGeneratorImageFilter.h"
namespace itk
{
namespace Functor
{
/**
* \class LandweberMethod
* \brief Functor class for computing a Landweber iteration.
* \ingroup ITKDeconvolution
*/
template <typename TInput1, typename TInput2, typename TInput3, typename TOutput>
class ITK_TEMPLATE_EXPORT LandweberMethod
{
public:
bool
operator==(const LandweberMethod &) const
{
return true;
}
ITK_UNEQUAL_OPERATOR_MEMBER_FUNCTION(LandweberMethod);
inline TOutput
operator()(const TInput1 & estimateFT, const TInput2 & kernelFT, const TInput2 & inputFT) const
{
return m_Alpha * std::conj(kernelFT) * inputFT +
(NumericTraits<typename TInput1::value_type>::OneValue() - m_Alpha * std::norm(kernelFT)) * estimateFT;
}
typename TInput1::value_type m_Alpha;
};
} // end namespace Functor
/**
* \class LandweberDeconvolutionImageFilter
* \brief Deconvolve an image using the Landweber deconvolution
* algorithm.
*
* This filter implements the Landweber deconvolution algorithm as
* defined in Bertero M and Boccacci P, "Introduction to Inverse
* Problems in Imaging", 1998. The algorithm assumes that the input
* image has been formed by a linear shift-invariant system with a
* known kernel.
*
* The Landweber algorithm converges to a solution that minimizes the
* sum of squared errors \f$||f \otimes h - g||\f$ where \f$f\f$ is
* the estimate of the unblurred image, \f$\otimes\f$ is the
* convolution operator, \f$h\f$ is the blurring kernel, and \f$g\f$ is
* the blurred input image. As such, it is best suited for images that
* have zero-mean Gaussian white noise.
*
* This is the base implementation of the Landweber algorithm. It may
* produce results with negative values. For a version of this
* algorithm that enforces a positivity constraint on each
* intermediate solution, see ProjectedLandweberDeconvolutionImageFilter.
*
* This code was adapted from the Insight Journal contribution:
*
* "Deconvolution: infrastructure and reference algorithms"
* by Gaetan Lehmann
* https://www.insight-journal.org/browse/publication/753
*
* \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
* \sa IterativeDeconvolutionImageFilter
* \sa RichardsonLucyDeconvolutionImageFilter
* \sa ProjectedLandweberDeconvolutionImageFilter
*/
template <typename TInputImage,
typename TKernelImage = TInputImage,
typename TOutputImage = TInputImage,
typename TInternalPrecision = double>
class ITK_TEMPLATE_EXPORT LandweberDeconvolutionImageFilter
: public IterativeDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(LandweberDeconvolutionImageFilter);
/** Standard type alias. */
using Self = LandweberDeconvolutionImageFilter;
using Superclass = IterativeDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Other useful type alias. */
using InputImageType = TInputImage;
using KernelImageType = TKernelImage;
using OutputImageType = TOutputImage;
/** Internal types used by the FFT filters. */
using typename Superclass::InternalImageType;
using typename Superclass::InternalImagePointerType;
using typename Superclass::InternalComplexType;
using typename Superclass::InternalComplexImageType;
using typename Superclass::InternalComplexImagePointerType;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(LandweberDeconvolutionImageFilter);
/** Set/get relaxation factor. */
itkSetMacro(Alpha, double);
itkGetMacro(Alpha, double);
protected:
LandweberDeconvolutionImageFilter();
~LandweberDeconvolutionImageFilter() override;
void
Initialize(ProgressAccumulator * progress, float progressWeight, float iterationProgressWeight) override;
void
Iteration(ProgressAccumulator * progress, float iterationProgressWeight) override;
void
Finish(ProgressAccumulator * progress, float progressWeight) override;
using typename Superclass::FFTFilterType;
using typename Superclass::IFFTFilterType;
void
PrintSelf(std::ostream & os, Indent indent) const override;
private:
double m_Alpha{};
InternalComplexImagePointerType m_TransformedInput{};
using LandweberFunctor =
Functor::LandweberMethod<InternalComplexType, InternalComplexType, InternalComplexType, InternalComplexType>;
using LandweberFilterType = TernaryGeneratorImageFilter<InternalComplexImageType,
InternalComplexImageType,
InternalComplexImageType,
InternalComplexImageType>;
typename LandweberFilterType::Pointer m_LandweberFilter{};
typename IFFTFilterType::Pointer m_IFFTFilter{};
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkLandweberDeconvolutionImageFilter.hxx"
#endif
#endif
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