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Adaptive Histogram Equalization Image Filter

Synopsis

Apply a power law adaptive histogram equalization controlled by the parameters alpha and beta.

The parameter alpha controls how much the filter acts like the classical histogram equalization method (alpha = 0) to how much the filter acts like an unsharp mask (alpha = 1).

The parameter beta controls how much the filter acts like an unsharp mask (beta = 0) to much the filter acts like pass through (beta = 1, with alpha = 1).

The parameter window (or radius) controls the size of the region over which local statistics are calculated.

Results

Input image.	Output image.
Input image histogram.	Output image histogram.

Code

C++

#include "itkAdaptiveHistogramEqualizationImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"

int
main(int argc, char * argv[])
{
  if (argc < 6)
  {
    std::cerr << "Missing parameters." << std::endl;
    std::cerr << "Usage: " << argv[0] << " inputImageFile outputImageFile alpha beta radius" << std::endl;
    return EXIT_FAILURE;
  }

  constexpr unsigned int Dimension = 2;

  using PixelType = unsigned char;
  using ImageType = itk::Image<PixelType, Dimension>;

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

  using AdaptiveHistogramEqualizationImageFilterType = itk::AdaptiveHistogramEqualizationImageFilter<ImageType>;
  AdaptiveHistogramEqualizationImageFilterType::Pointer adaptiveHistogramEqualizationImageFilter =
    AdaptiveHistogramEqualizationImageFilterType::New();

  float alpha = std::stod(argv[3]);
  adaptiveHistogramEqualizationImageFilter->SetAlpha(alpha);

  float beta = std::stod(argv[4]);
  adaptiveHistogramEqualizationImageFilter->SetBeta(beta);

  int  radiusSize = std::stoi(argv[5]);
  auto radius = AdaptiveHistogramEqualizationImageFilterType::ImageSizeType::Filled(radiusSize);
  adaptiveHistogramEqualizationImageFilter->SetRadius(radius);

  adaptiveHistogramEqualizationImageFilter->SetInput(input);

  adaptiveHistogramEqualizationImageFilter->Update();

  itk::WriteImage(adaptiveHistogramEqualizationImageFilter->GetOutput(), argv[2]);

  return EXIT_SUCCESS;
}

Python

#!/usr/bin/env python

import itk
import argparse

parser = argparse.ArgumentParser(
    description="Adaptive Histogram Equalization Image Filter."
)
parser.add_argument("input_image")
parser.add_argument("output_image")
parser.add_argument("alpha", type=float)
parser.add_argument("beta", type=float)
parser.add_argument("radius", type=int)
args = parser.parse_args()

Dimension = 2

PixelType = itk.ctype("unsigned char")
ImageType = itk.Image[PixelType, Dimension]

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

histogramEqualization = itk.AdaptiveHistogramEqualizationImageFilter.New(reader)
histogramEqualization.SetAlpha(args.alpha)
histogramEqualization.SetBeta(args.beta)

radius = itk.Size[Dimension]()
radius.Fill(args.radius)
histogramEqualization.SetRadius(radius)

itk.imwrite(histogramEqualization, args.output_image)

Classes demonstrated

template<typename TImageType, typename TKernel = Neighborhood<bool, TImageType::ImageDimension>>
class AdaptiveHistogramEqualizationImageFilter : public itk::MovingHistogramImageFilter<TImageType, TImageType, Neighborhood<bool, TImageType::ImageDimension>, Function::AdaptiveEqualizationHistogram<TImageType::PixelType, TImageType::PixelType>>

Power Law Adaptive Histogram Equalization.

Histogram equalization modifies the contrast in an image. The AdaptiveHistogramEqualizationImageFilter is a superset of many contrast enhancing filters. By modifying its parameters (alpha, beta, and window), the AdaptiveHistogramEqualizationImageFilter can produce an adaptively equalized histogram or a version of unsharp mask (local mean subtraction). Instead of applying a strict histogram equalization in a window about a pixel, this filter prescribes a mapping function (power law) controlled by the parameters alpha and beta.

The parameter alpha controls how much the filter acts like the classical histogram equalization method (alpha=0) to how much the filter acts like an unsharp mask (alpha=1).

The parameter beta controls how much the filter acts like an unsharp mask (beta=0) to much the filter acts like pass through (beta=1, with alpha=1).

The parameter window controls the size of the region over which local statistics are calculated. The size of the window is controlled by SetRadius the default Radius is 5 in all directions.

By altering alpha, beta and window, a host of equalization and unsharp masking filters is available.

The boundary condition ignores the part of the neighborhood outside the image, and over-weights the valid part of the neighborhood.

For a detailed description see [120].

ITK Sphinx Examples:

• All ITK Sphinx Examples

• Adaptive Histogram Equalization Image Filter

See itk::AdaptiveHistogramEqualizationImageFilter for additional documentation.