Image2DConvolver.h

Classes

Image2DConvolver -- This class does 2D convolution of an image by a functional form (full description)

template <class T> class Image2DConvolver

Interface

Public Members

Image2DConvolver ()

Image2DConvolver(const Image2DConvolver<T> &other)

~Image2DConvolver()

Image2DConvolver &operator=(const Image2DConvolver<T> &other)

void convolve(LogIO& os, ImageInterface<T>& imageOut, ImageInterface<T>& imageIn, T::KernelTypes kernelType, const IPosition& pixelAxes, const Kernelypes<IPosition<Double> >& parameters, Bool autoScale, Double scale, Bool copyMiscellaneous=True)

Private Members

void checkKernelParameters(LogIO& os, VectorKernel::KernelTypes kernelType, const Vector<Vector<Double> >& parameters) const

void dealWithRestoringBeam (LogIO& os, String& brightnessUnitOut, Vector<Quantum<Double> >& beamOut, Array<T>& kernelArray, T kernelVolume, VectorKernel::KernelTypes kernelType, const Vector<Quantum<Double> >& parameters, const IPosition& axes, const ypes& cSys, const IPosition& imageInfo, const CoordinateSystem& brightnessUnit, Bool autoscale, Double scale) const

T fillKernel (Matrix<T>& kernelMatrix, Matrix::KernelTypes kernelType, const IPosition& kernelShape, const IPosition& axes, const Kernelypes<Double>& parameters) const

void fillGaussian (T& maxVal, T& volume, Matrix<T>& pixels, T height, T xCentre, T yCentre, T majorAxis, T ratio, T positionAngle) const

T makeKernel(LogIO& os, LogIO<T>& kernel, Array::KernelTypes kernelType, const Vector<Kernelypes<Double> >& parameters, const Vector& axes, const ImageInterface<T>& inImage) const

IPosition shapeOfKernel (VectorKernel::KernelTypes kernelType, const Vector<Double>& parameters, uInt ndim, const IPosition& axes) const

uInt sizeOfGaussian (Double width, Double nSigma) const

Description

Review Status

Date Reviewed:: yyyy/mm/dd

Prerequisite

Etymology

This class handles 2D convolution of images

Synopsis

This class convolves an image by a specified 2D function.

Example

Motivation

Convolution is a standard image processing requirement. The class object has no state. The functions could be static. The convolution is done via FFT. Thus input pixels which are masked are set to 0 before the convolution. The mask is transferred to the output image. No additional scaling of the output image values is done.

To Do

Member Description