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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 itkWienerDeconvolutionImageFilter_hxx
#define itkWienerDeconvolutionImageFilter_hxx
#include "itkBinaryGeneratorImageFilter.h"
namespace itk
{
template <typename TInputImage, typename TKernelImage, typename TOutputImage, typename TInternalPrecision>
WienerDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>::
WienerDeconvolutionImageFilter()
{
m_NoiseVariance = 0.0;
}
template <typename TInputImage, typename TKernelImage, typename TOutputImage, typename TInternalPrecision>
void
WienerDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>::GenerateData()
{
// Create a process accumulator for tracking the progress of this
// minipipeline
auto progress = ProgressAccumulator::New();
progress->SetMiniPipelineFilter(this);
auto localInput = InputImageType::New();
localInput->Graft(this->GetInput());
const KernelImageType * kernelImage = this->GetKernelImage();
InternalComplexImagePointerType input = nullptr;
InternalComplexImagePointerType kernel = nullptr;
this->PrepareInputs(localInput, kernelImage, input, kernel, progress, 0.7);
using FunctorType = Functor::WienerDeconvolutionFunctor<InternalComplexType>;
FunctorType wienerFunctor;
wienerFunctor.SetNoisePowerSpectralDensityConstant(m_NoiseVariance);
wienerFunctor.SetKernelZeroMagnitudeThreshold(this->GetKernelZeroMagnitudeThreshold());
using WienerFilterType =
BinaryGeneratorImageFilter<InternalComplexImageType, InternalComplexImageType, InternalComplexImageType>;
auto wienerFilter = WienerFilterType::New();
wienerFilter->SetInput(0, input);
wienerFilter->SetInput(1, kernel);
wienerFilter->SetFunctor(wienerFunctor);
wienerFilter->ReleaseDataFlagOn();
progress->RegisterInternalFilter(wienerFilter, 0.1);
// Free up the memory for the prepared inputs
input = nullptr;
kernel = nullptr;
this->ProduceOutput(wienerFilter->GetOutput(), progress, 0.2);
}
template <typename TInputImage, typename TKernelImage, typename TOutputImage, typename TInternalPrecision>
void
WienerDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>::PrintSelf(
std::ostream & os,
Indent indent) const
{
this->Superclass::PrintSelf(os, indent);
os << indent << "NoiseVariance: " << m_NoiseVariance << std::endl;
}
} // end namespace itk
#endif
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