VTK:频率处理——高通滤波(理想)

1.理想高通滤波

高通滤波与低通滤波正好相反,是频率图像的高频部分通过抑制低频部分。在图像中图像的边缘对应高频分量,因此高通滤波的效果图是图像锐化。同样是最简单的高通滤波是理想的高通滤波器。通过设置一频率阈值,将高于阈值的频率通过,而低于预知的低频部分设置为0.

2.代码

#include "Test.h"
#include 
VTK_MODULE_INIT(vtkRenderingOpenGL2);
VTK_MODULE_INIT(vtkInteractionStyle);

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

int main(int argc, char* argv[])
{
	vtkSmartPointer<vtkJPEGReader> reader =
		vtkSmartPointer<vtkJPEGReader>::New();
	reader->SetFileName("data/Lena.jpg");
	reader->Update();

	vtkSmartPointer<vtkImageFFT> fftFilter =
		vtkSmartPointer<vtkImageFFT>::New();
	fftFilter->SetInputConnection(reader->GetOutputPort());
	fftFilter->Update();

	vtkSmartPointer<vtkImageIdealHighPass> highPassFilter =
		vtkSmartPointer<vtkImageIdealHighPass>::New();
	highPassFilter->SetInputConnection(fftFilter->GetOutputPort());
	highPassFilter->SetXCutOff(0.1);
	highPassFilter->SetYCutOff(0.1);
	highPassFilter->Update();

	vtkSmartPointer<vtkImageRFFT> rfftFilter =
		vtkSmartPointer<vtkImageRFFT>::New();
	rfftFilter->SetInputConnection(highPassFilter->GetOutputPort());
	rfftFilter->Update();

	vtkSmartPointer<vtkImageExtractComponents> ifftExtractReal =
		vtkSmartPointer<vtkImageExtractComponents>::New();
	ifftExtractReal->SetInputConnection(rfftFilter->GetOutputPort());
	ifftExtractReal->SetComponents(0);

	vtkSmartPointer<vtkImageCast> castFilter =
		vtkSmartPointer<vtkImageCast>::New();
	castFilter->SetInputConnection(ifftExtractReal->GetOutputPort());
	castFilter->SetOutputScalarTypeToUnsignedChar();
	castFilter->Update();
	//
		vtkSmartPointer<vtkImageActor> originalActor =
		vtkSmartPointer<vtkImageActor>::New();
	originalActor->SetInputData(reader->GetOutput());

	vtkSmartPointer<vtkImageActor> erodedActor =
		vtkSmartPointer<vtkImageActor>::New();
	erodedActor->SetInputData(castFilter->GetOutput());
	//
		double leftViewport[4] = { 0.0, 0.0, 0.5, 1.0 };
	double rightViewport[4] = { 0.5, 0.0, 1.0, 1.0 };
	vtkSmartPointer<vtkRenderer> leftRenderer =
		vtkSmartPointer<vtkRenderer>::New();
	leftRenderer->AddActor(originalActor);
	leftRenderer->SetViewport(leftViewport);
	leftRenderer->SetBackground(1.0, 1.0, 1.0);
	leftRenderer->ResetCamera();
	vtkSmartPointer<vtkRenderer> rightRenderer =
		vtkSmartPointer<vtkRenderer>::New();
	rightRenderer->AddActor(erodedActor);
	rightRenderer->SetViewport(rightViewport);
	rightRenderer->SetBackground(1.0, 1.0, 1.0);
	rightRenderer->ResetCamera();
	//
		vtkSmartPointer<vtkRenderWindow> rw =
		vtkSmartPointer<vtkRenderWindow>::New();
	rw->SetSize(640, 320);
	rw->AddRenderer(leftRenderer);
	rw->AddRenderer(rightRenderer);
	rw->SetWindowName("IdealHighPassExample");

	vtkSmartPointer<vtkRenderWindowInteractor> rwi =
		vtkSmartPointer<vtkRenderWindowInteractor>::New();
	vtkSmartPointer<vtkInteractorStyleImage> style =
		vtkSmartPointer<vtkInteractorStyleImage>::New();
	rwi->SetInteractorStyle(style);
	rwi->SetRenderWindow(rw);
	rwi->Start();

	return 0;
}

3.运行结果

VTK:频率处理——高通滤波(理想)_第1张图片
同低频滤波一样,首先将读入图像vtkImageFFT转换到频率空间,定义 vtkImageIdealHighPass对象,并通过SetCutOff()和SetYOff()设置X和Y方向的截止频率。然后通过vtkImageRFFT将处理后的图像转换到空域中,得到高通滤波图像。为了显示的需要,还需要提取图像分量和数据类型的转换。

你可能感兴趣的:(计算机视觉,opencv,人工智能)