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Perona Malik Anisotropic Diffusion on Grayscale Image

Synopsis

Perona Malik Anisotropic Diffusion for scalar valued images.

Results

Before anisotropic diffusion (left) and after anisotropic diffusion (right).

Code

Python

#!/usr/bin/env python

import itk
import argparse

parser = argparse.ArgumentParser(
    description="Compute Perona Malik Anisotropic Diffusion."
)
parser.add_argument("input_image")
parser.add_argument("output_image")
parser.add_argument("number_of_iterations", type=int)
parser.add_argument("conductance", type=float)
args = parser.parse_args()


Dimension = 2
InputPixelType = itk.UC
InputImageType = itk.Image[InputPixelType, Dimension]
OutputPixelType = itk.F
OutputImageType = itk.Image[OutputPixelType, Dimension]

ReaderType = itk.ImageFileReader[InputImageType]
reader = ReaderType.New()
reader.SetFileName(args.input_image)

CastFilterType = itk.CastImageFilter[InputImageType, OutputImageType]
castfilter = CastFilterType.New()
castfilter.SetInput(reader)

FilterType = itk.GradientAnisotropicDiffusionImageFilter[
    OutputImageType, OutputImageType
]
gradientfilter = FilterType.New()
gradientfilter.SetInput(castfilter.GetOutput())
gradientfilter.SetNumberOfIterations(args.number_of_iterations)
gradientfilter.SetTimeStep(0.125)
gradientfilter.SetConductanceParameter(args.conductance)

WriterType = itk.ImageFileWriter[OutputImageType]
writer = WriterType.New()
writer.SetFileName(args.output_image)
writer.SetInput(gradientfilter.GetOutput())

writer.Update()

C++

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

int
main(int argc, char * argv[])
{
  if (argc != 5)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0];
    std::cerr << " <InputFileName>";
    std::cerr << " <OutputFileName>";
    std::cerr << " <NumberOfIterations> ";
    std::cerr << " <Conductance>" << std::endl;
    return EXIT_FAILURE;
  }

  constexpr unsigned int Dimension = 2;

  using InputPixelType = unsigned char;
  using InputImageType = itk::Image<InputPixelType, Dimension>;

  const auto input = itk::ReadImage<InputImageType>(argv[1]);

  using OutputPixelType = float;
  using OutputImageType = itk::Image<OutputPixelType, Dimension>;
  using FilterType = itk::GradientAnisotropicDiffusionImageFilter<InputImageType, OutputImageType>;
  auto filter = FilterType::New();
  filter->SetInput(input);
  filter->SetNumberOfIterations(std::stoi(argv[3]));
  filter->SetTimeStep(0.125);
  filter->SetConductanceParameter(std::stod(argv[4]));

  try
  {
    itk::WriteImage(filter->GetOutput(), argv[2]);
  }
  catch (const itk::ExceptionObject & error)
  {
    std::cerr << "Error: " << error << std::endl;
    return EXIT_FAILURE;
  }

  return EXIT_SUCCESS;
}

Classes demonstrated

template<typename TInputImage, typename TOutputImage>
class GradientAnisotropicDiffusionImageFilter : public itk::AnisotropicDiffusionImageFilter<TInputImage, TOutputImage>

This filter performs anisotropic diffusion on a scalar itk::Image using the classic Perona-Malik, gradient magnitude based equation.

For detailed information on anisotropic diffusion, see itkAnisotropicDiffusionFunction and itkGradientNDAnisotropicDiffusionFunction.

Inputs and Outputs

The input to this filter should be a scalar itk::Image of any dimensionality. The output image will be a diffused copy of the input.

Parameters

Please see the description of parameters given in itkAnisotropicDiffusionImageFilter.

See

AnisotropicDiffusionImageFilter

See

AnisotropicDiffusionFunction

See

GradientAnisotropicDiffusionFunction

See itk::GradientAnisotropicDiffusionImageFilter for additional documentation.