Examples/Filtering/FFTImageFilterFourierDomainFiltering.cxx

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//  Software Guide : BeginLatex
//
//  One of the most common image processing operations performed in the Fourier
//  Domain is the masking of the spectrum in order to eliminate a range of
//  spatial frequencies from the input image. This operation is typically
//  performed by taking the input image, computing its Fourier transform using
//  a FFT filter, masking the resulting image in the Fourier domain with a
//  mask, and finally taking the result of the masking and computing its
//  inverse Fourier transform.
//
//  This typical process is illustrated in the example below.
//
//  \index{itk::Forward\-FFT\-Image\-Filter}
//  \index{itk::Vnl\-Forward\-FFT\-Image\-Filter}
//  \index{itk::FFTW\-Forward\-FFT\-Image\-Filter}
//  \index{itk::Mask\-Image\-Filter}
//
//  Software Guide : EndLatex


#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"

// Software Guide : BeginLatex
//
// We start by including the headers of the FFT filters and the Mask image
// filter. Note that we use two different types of FFT filters here. The first
// one expects as input an image of real pixel type (real in the sense of
// complex numbers) and produces as output a complex image. The second FFT
// filter expects as in put a complex image and produces a real image as
// output.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkVnlForwardFFTImageFilter.h"
#include "itkVnlInverseFFTImageFilter.h"
#include "itkMaskImageFilter.h"
// Software Guide : EndCodeSnippet


int main( int argc, char * argv [] )
{
  if( argc < 4 )
    {
    std::cerr << "Usage: " << argv[0] << " inputScalarImage  inputMaskImage";
    std::cerr << " outputFilteredImage" << std::endl;
    }

  // Software Guide : BeginLatex
  //
  // The first decision to make is related to the pixel type and dimension of the
  // images on which we want to compute the Fourier transform.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef float  InputPixelType;
  const unsigned int Dimension = 2;

  typedef itk::Image< InputPixelType, Dimension > InputImageType;
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // Then we select the pixel type to use for the mask image and instantiate the
  // image type of the mask.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef unsigned char  MaskPixelType;

  typedef itk::Image< MaskPixelType, Dimension > MaskImageType;
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // Both the input image and the mask image can be read from files or could be
  // obtained as the output of a preprocessing pipeline. We omit here the details
  // of reading the image since the process is quite standard.
  //
  // Software Guide : EndLatex

  typedef itk::ImageFileReader< InputImageType >    InputReaderType;
  typedef itk::ImageFileReader< MaskImageType  >    MaskReaderType;

  InputReaderType::Pointer inputReader = InputReaderType::New();
  MaskReaderType::Pointer  maskReader  = MaskReaderType::New();

  inputReader->SetFileName( argv[1] );
  maskReader->SetFileName( argv[2] );

  // Software Guide : BeginLatex
  //
  // Now the \doxygen{VnlForwardFFTImageFilter} can be instantiated.
  // Like most ITK filters, the FFT filter is instantiated using the full image type.
  // By not setting the output image type, we decide to use the default one provided
  // by the filter. Using this type we construct one instance of the filter.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef itk::VnlForwardFFTImageFilter< InputImageType >  FFTFilterType;

  FFTFilterType::Pointer fftFilter = FFTFilterType::New();

  fftFilter->SetInput( inputReader->GetOutput() );
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // Since our purpose is to perform filtering in the frequency domain by
  // altering the weights of the image spectrum, we need a filter that will
  // mask the Fourier transform of the input image with a binary image. Note that the
  // type of the spectral image is taken here from the traits of the FFT filter.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef FFTFilterType::OutputImageType    SpectralImageType;

  typedef itk::MaskImageFilter< SpectralImageType,
                                    MaskImageType,
                                    SpectralImageType >  MaskFilterType;

  MaskFilterType::Pointer maskFilter = MaskFilterType::New();
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // We connect the inputs to the mask filter by taking the outputs from the
  // first FFT filter and from the reader of the Mask image.
  //
  // Software Guide : EndLatex


  // Software Guide : BeginCodeSnippet
  maskFilter->SetInput1( fftFilter->GetOutput() );
  maskFilter->SetInput2( maskReader->GetOutput() );
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // For the purpose of verifying the aspect of the spectrum after being filtered
  // with the mask, we can write out the output of the Mask filter to a file.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef itk::ImageFileWriter< SpectralImageType > SpectralWriterType;
  SpectralWriterType::Pointer spectralWriter = SpectralWriterType::New();
  spectralWriter->SetFileName("filteredSpectrum.mhd");
  spectralWriter->SetInput( maskFilter->GetOutput() );
  spectralWriter->Update();
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // The output of the mask filter will contain the \emph{filtered} spectrum
  // of the input image. We must then apply an inverse Fourier transform on it in
  // order to obtain the filtered version of the input image. For that purpose we
  // create another instance of the FFT filter.
  //
  // Software Guide : EndLatex


  // Software Guide : BeginCodeSnippet
  typedef itk::VnlInverseFFTImageFilter<
    SpectralImageType >  IFFTFilterType;

  IFFTFilterType::Pointer fftInverseFilter = IFFTFilterType::New();

  fftInverseFilter->SetInput( maskFilter->GetOutput() );
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // The execution of the pipeline can be triggered by invoking the
  // \code{Update()} method in this last filter.  Since this invocation can
  // eventually throw an exception, the call must be placed inside a try/catch
  // block.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  try
    {
    fftInverseFilter->Update();
    }
  catch( itk::ExceptionObject & excp )
    {
    std::cerr << "Error: " << std::endl;
    std::cerr << excp << std::endl;
    return EXIT_FAILURE;
    }
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // The result of the filtering can now be saved into an image file, or be
  // passed to a subsequent processing pipeline. Here we simply write it out to
  // an image file.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef itk::ImageFileWriter< InputImageType > WriterType;
  WriterType::Pointer writer = WriterType::New();
  writer->SetFileName( argv[3] );
  writer->SetInput( fftInverseFilter->GetOutput() );
  // Software Guide : EndCodeSnippet


  try
    {
    writer->Update();
    }
  catch( itk::ExceptionObject & excp )
    {
    std::cerr << "Error writing the real image: " << std::endl;
    std::cerr << excp << std::endl;
    return EXIT_FAILURE;
    }

  // Software Guide : BeginLatex
  //
  // Note that this example is just a minimal illustration of the multiple types
  // of processing that are possible in the Fourier domain.
  //
  //  Software Guide : EndLatex

  return EXIT_SUCCESS;
}