影象濾波----低通濾波，中值濾波，高通濾波，方向濾波（Sobel），拉普拉斯變換
阿新 • • 發佈：2019-01-04
①觀察灰度分佈來描述一幅影象成為空間域，觀察影象變化的頻率被成為頻域。
②頻域分析：低頻對應區域的影象強度變化緩慢，高頻對應的變化快。低通濾波器去除了影象的高頻部分，高通濾波器去除了影象的低頻部分。
(1)低通濾波
①栗子：
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
int main()
{
// Read input image 結果：每個畫素變為相鄰畫素的平均值， 快速的強度變化轉化為平緩的過度
②栗子：近的畫素新增更多的權重。：高斯濾波器
cv::GaussianBlur(image,result,cv::Size(5,5),1.5);
(2)中值濾波 ：非線性濾波
有效去除椒鹽噪點
cv::medianBlur(image,result,5);
（3）方向濾波（Sobel）
強調影象中的高頻分量，使用高通濾波器進行邊緣檢測。
Sobel運算元是一種經典的邊緣檢測線性濾波器，可被認為是影象在垂直和水平方向變化的測量。
#include <iostream>
#include <iomanip>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include "laplacianZC.h"
int main()
{
//Read input image
cv::Mat image= cv::imread("boldt.jpg",0);
if (!image.data)
return 0;
// Display the image
cv::namedWindow("Original Image");
cv::imshow("Original Image",image);
// Compute Sobel X derivative
cv::Mat sobelX;
cv::Sobel(image,sobelX,CV_8U,1,0,3,0.4,128);
// Display the image
cv::namedWindow("Sobel X Image");
cv::imshow("Sobel X Image",sobelX);
// Compute Sobel Y derivative
cv::Mat sobelY;
cv::Sobel(image,sobelY,CV_8U,0,1,3,0.4,128);
// Display the image
cv::namedWindow("Sobel Y Image");
cv::imshow("Sobel Y Image",sobelY);
// Compute norm of Sobel 得到sobel的摸
cv::Sobel(image,sobelX,CV_16S,1,0);
cv::Sobel(image,sobelY,CV_16S,0,1);
cv::Mat sobel;
//compute the L1 norm
sobel= abs(sobelX)+abs(sobelY);
double sobmin, sobmax;
cv::minMaxLoc(sobel,&sobmin,&sobmax);
std::cout << "sobel value range: " << sobmin << " " << sobmax << std::endl;
// Print window pixel values
for (int i=0; i<12; i++) {
for (int j=0; j<12; j++)
std::cout << std::setw(5) << static_cast<int>(sobel.at<short>(i+135,j+362)) << " ";
std::cout << std::endl;
}
std::cout << std::endl;
std::cout << std::endl;
std::cout << std::endl;
// Conversion to 8-bit image
// sobelImage = -alpha*sobel + 255
cv::Mat sobelImage;
sobel.convertTo(sobelImage,CV_8U,-255./sobmax,255);
// Display the image
cv::namedWindow("Sobel Image");
cv::imshow("Sobel Image",sobelImage);
// Apply threshold to Sobel norm (low threshold value)
cv::Mat sobelThresholded;
cv::threshold(sobelImage, sobelThresholded, 225, 255, cv::THRESH_BINARY);
// Display the image
cv::namedWindow("Binary Sobel Image (low)");
cv::imshow("Binary Sobel Image (low)",sobelThresholded);
// Apply threshold to Sobel norm (high threshold value)
cv::threshold(sobelImage, sobelThresholded, 190, 255, cv::THRESH_BINARY);
// Display the image
cv::namedWindow("Binary Sobel Image (high)");
cv::imshow("Binary Sobel Image (high)",sobelThresholded);結果：
（4）影象的拉普拉斯變換
是一種基於影象導數的高通線性濾波器，計算二階倒數已衡量影象的彎曲度。
// Compute Laplacian 3x3
cv::Mat image = cv::imread("boldt.jpg", 0);
cv::Mat laplace;
cv::Laplacian(image,laplace,CV_8U,1,1,128);
// Display the image
cv::namedWindow("Laplacian Image");
cv::imshow("Laplacian Image",laplace);
// Print window pixel values
for (int i=0; i<12; i++) {
for (int j=0; j<12; j++)
std::cout << std::setw(5) << static_cast<int>(laplace.at<uchar>(i+135,j+362))-128 << " ";
std::cout << std::endl;
}
std::cout << std::endl;
std::cout << std::endl;
std::cout << std::endl;
// Compute Laplacian 7x7
cv::Laplacian(image,laplace,CV_8U,7,0.01,128);
// Display the image
cv::namedWindow("Laplacian Image");
cv::imshow("Laplacian Image",laplace);
// Print window pixel values
for (int i=0; i<12; i++) {
for (int j=0; j<12; j++)
std::cout << std::setw(5) << static_cast<int>(laplace.at<uchar>(i+135,j+362))-128 << " ";
std::cout << std::endl;
}
// Extract small window
cv::Mat window(image,cv::Rect(362,135,12,12));
cv::namedWindow("Image window");
cv::imshow("Image window",window);
cv::imwrite("window.bmp",window);
// Compute Laplacian using LaplacianZC class
LaplacianZC laplacian;
laplacian.setAperture(7);
cv::Mat flap= laplacian.computeLaplacian(image);
double lapmin, lapmax;
cv::minMaxLoc(flap,&lapmin,&lapmax);
std::cout << "Laplacian value range=[" << lapmin << "," << lapmax << "]\n";
laplace= laplacian.getLaplacianImage();
cv::namedWindow("Laplacian Image (7x7)");
cv::imshow("Laplacian Image (7x7)",laplace);
// Print Laplacian values
std::cout << std::endl;
for (int i=0; i<12; i++) {
for (int j=0; j<12; j++)
std::cout << std::setw(5) << static_cast<int>(flap.at<float>(i+135,j+362)/100) << " ";
std::cout << std::endl;
}
std::cout << std::endl;
// Compute and display the zero-crossing points
cv::Mat zeros;
zeros= laplacian.getZeroCrossings(lapmax);
cv::namedWindow("Zero-crossings");
cv::imshow("Zero-crossings",zeros);
// Compute and display the zero-crossing points (Sobel version)
zeros= laplacian.getZeroCrossings();
zeros= laplacian.getZeroCrossingsWithSobel(50);
cv::namedWindow("Zero-crossings (2)");
cv::imshow("Zero-crossings (2)",zeros);
// Print window pixel values
for (int i=0; i<12; i++) {
for (int j=0; j<12; j++)
std::cout << std::setw(2) << static_cast<int>(zeros.at<uchar>(i+135,j+362)) << " ";
std::cout << std::endl;
}
// Display the image with window
cv::rectangle(image,cv::Point(362,135),cv::Point(374,147),cv::Scalar(255,255,255));
cv::namedWindow("Original Image with window");
cv::imshow("Original Image with window",image);
cv::waitKey();
return 0;
}
