Class WienerDeconvolutionImageFilter

java.lang.Object
org.itk.simple.ProcessObject
org.itk.simple.ImageFilter
org.itk.simple.WienerDeconvolutionImageFilter

public class WienerDeconvolutionImageFilter extends ImageFilter
The Wiener deconvolution image filter is designed to restore an image convolved with a blurring kernel while keeping noise enhancement to a minimum. The Wiener filter aims to minimize noise enhancement induced by frequencies with low signal-to-noise ratio. The Wiener filter kernel is defined in the frequency domain as $W(\\omega) = H^*(\\omega) / (|H(\\omega)|^2 + (1 / SNR(\\omega)))$ where $H(\\omega)$ is the Fourier transform of the blurring kernel with which the original image was convolved and the signal-to-noise ratio $SNR(\\omega)$ . $SNR(\\omega)$ is defined by $P_f(\\omega) / P_n(\\omega)$ where $P_f(\\omega)$ is the power spectral density of the uncorrupted signal and $P_n(\\omega)$ is the power spectral density of the noise. When applied to the input blurred image, this filter produces an estimate $\\hat{f}(x)$ of the true underlying signal $f(x)$ that minimizes the expected error between $\\hat{f}(x)$ and $f(x)$ . This filter requires two inputs, the image to be deconvolved and the blurring kernel. These two inputs can be set using the methods SetInput() and SetKernelImage(), respectively. The power spectral densities of the signal and noise are typically unavailable for a given problem. In particular, $P_f(\\omega)$ cannot be computed from $f(x)$ because this unknown signal is precisely the signal that this filter aims to recover. Nevertheless, it is common for the noise to have a power spectral density that is flat or decreasing significantly more slowly than the power spectral density of a typical image as the frequency $\\omega$ increases. Hence, $P_n(\\omega)$ can typically be approximated with a constant, and this filter makes this assumption (see the NoiseVariance member variable). $P_f(\\omega)$ , on the other hand, will vary with input. This filter computes the power spectral density of the input blurred image, subtracts the power spectral density of the noise, and uses the result as the estimate of $P_f(\\omega)$ . For further information on the Wiener deconvolution filter, please see "Digital Signal Processing" by Kenneth R. Castleman, Prentice Hall, 1995 Gaetan Lehmann, Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France Chris Mullins, The University of North Carolina at Chapel Hill Cory Quammen, The University of North Carolina at Chapel Hill See: itk::simple::WienerDeconvolution for the procedural interface itk::WienerDeconvolutionImageFilter for the Doxygen on the original ITK class. C++ includes: sitkWienerDeconvolutionImageFilter.h
  • Constructor Details

    • WienerDeconvolutionImageFilter

      protected WienerDeconvolutionImageFilter(long cPtr, boolean cMemoryOwn)
    • WienerDeconvolutionImageFilter

      public WienerDeconvolutionImageFilter()
      itk::simple::WienerDeconvolutionImageFilter::WienerDeconvolutionImageFilter() Default Constructor that takes no arguments and initializes default parameters
  • Method Details

    • getCPtr

      protected static long getCPtr(WienerDeconvolutionImageFilter obj)
    • swigRelease

      protected static long swigRelease(WienerDeconvolutionImageFilter obj)
    • finalize

      protected void finalize()
      Overrides:
      finalize in class ImageFilter
    • delete

      public void delete()
      virtual itk::simple::WienerDeconvolutionImageFilter::~WienerDeconvolutionImageFilter() Destructor
      Overrides:
      delete in class ImageFilter
    • setNoiseVariance

      public void setNoiseVariance(double NoiseVariance)
      Self& itk::simple::WienerDeconvolutionImageFilter::SetNoiseVariance(double NoiseVariance) Set/get the variance of the zero-mean Gaussian white noise assumed to be added to the input.
    • getNoiseVariance

      public double getNoiseVariance()
      double itk::simple::WienerDeconvolutionImageFilter::GetNoiseVariance() const Set/get the variance of the zero-mean Gaussian white noise assumed to be added to the input.
    • setNormalize

      public void setNormalize(boolean Normalize)
      Self& itk::simple::WienerDeconvolutionImageFilter::SetNormalize(bool Normalize) Normalize the output image by the sum of the kernel components
    • normalizeOn

      public void normalizeOn()
      Self& itk::simple::WienerDeconvolutionImageFilter::NormalizeOn() Set the value of Normalize to true or false respectfully.
    • normalizeOff

      public void normalizeOff()
      Self& itk::simple::WienerDeconvolutionImageFilter::NormalizeOff()
    • getNormalize

      public boolean getNormalize()
      bool itk::simple::WienerDeconvolutionImageFilter::GetNormalize() const
    • setBoundaryCondition

      public void setBoundaryCondition(WienerDeconvolutionImageFilter.BoundaryConditionType BoundaryCondition)
      Self& itk::simple::WienerDeconvolutionImageFilter::SetBoundaryCondition(BoundaryConditionType BoundaryCondition)
    • getBoundaryCondition

      BoundaryConditionType itk::simple::WienerDeconvolutionImageFilter::GetBoundaryCondition() const
    • setOutputRegionMode

      public void setOutputRegionMode(WienerDeconvolutionImageFilter.OutputRegionModeType OutputRegionMode)
      Self& itk::simple::WienerDeconvolutionImageFilter::SetOutputRegionMode(OutputRegionModeType OutputRegionMode)
    • getOutputRegionMode

      OutputRegionModeType itk::simple::WienerDeconvolutionImageFilter::GetOutputRegionMode() const
    • getName

      public String getName()
      std::string itk::simple::WienerDeconvolutionImageFilter::GetName() const Name of this class
      Overrides:
      getName in class ProcessObject
    • toString

      public String toString()
      std::string itk::simple::WienerDeconvolutionImageFilter::ToString() const Print ourselves out
      Overrides:
      toString in class ProcessObject
    • execute

      public Image execute(Image image1, Image image2)
      Image itk::simple::WienerDeconvolutionImageFilter::Execute(const Image &image1, const Image &image2) Execute the filter on the input images