自己写图像锐化函数：
#include <iostream>#include <opencv2/core.hpp>#include <opencv2/highgui.hpp>#include <opencv2/imgproc.hpp>using namespace std;using namespace cv;void Sharpen(const Mat& myImage, Mat& Result);int main(){Mat srcImage = imread("1.png");//判断图像是否加载成功if(srcImage.data)cout << "图像加载成功!" << endl << endl;else{cout << "图像加载失败!" << endl << endl;return -1;}namedWindow("srcImage", WINDOW_AUTOSIZE);imshow("srcImage", srcImage);Mat dstImage;dstImage.create(srcImage.size(), srcImage.type());Sharpen(srcImage, dstImage);namedWindow("dstImage",WINDOW_AUTOSIZE);imshow("dstImage",dstImage);waitKey(0);return 0;}void Sharpen(const Mat& myImage, Mat& Result){CV_Assert(myImage.depth() == CV_8U); //判断函数CV_Assertconst int nChannels = myImage.channels();for(int j = 1; j < myImage.rows - 1; ++j){const uchar* precious = myImage.ptr<uchar>(j - 1);//当前像素上一行指针const uchar* current = myImage.ptr<uchar>(j); //当前像素行指针const uchar* next = myImage.ptr<uchar>(j + 1);//当前像素下一行指针uchar* output = Result.ptr<uchar>(j);//利用公式和上下左右四个像素对当前像素值进行处理for(int i = nChannels; i < nChannels * (myImage.cols - 1); ++i){// 0, -1 ,0;-1, 5, -1; 0, -1, 0;*output++ = saturate_cast<uchar>(5 * current[i] -current[i-nChannels]-current[i+nChannels]-precious[i]-next[i]);}}Result.row(0).setTo(Scalar(0)); //设置第一行所有元素值为0Result.row(Result.rows-1).setTo(Scalar(0));//设置最后一行所有元素值为0Result.col(0).setTo(Scalar(0)); //设置第一列所有元素值为0Result.col(Result.cols-1).setTo(Scalar(0));//设置最后一列所有元素值为0}
上面代码是以卷积核为[0−10−15−10−10]\begin{bmatrix} 0&-1&0\\ -1&5&-1 \\ 0&-1&0 \end{bmatrix}⎣⎡0−10−15−10−10⎦⎤为例的锐化，结果图就是轻微的锐化，这里不做展示。
图像卷积运算API函数：cv::filter2D()
举例： 直接使用边缘检测的拉普拉斯算子API函数，与自己定义拉普拉斯算子核使用cv::filter2D()的效果对比：
#include <iostream>#include <string>#include <vector>#include "opencv2/highgui/highgui.hpp"#include "opencv2/opencv.hpp"// g++ test.cpp `pkg-config opencv --libs --cflags` -std=c++11 -pthread -o testusing namespace std;using namespace cv;const int Kenel_s = 3; //卷积核大小int main() {//读入图片Mat src, dst, dst_L;src = imread("1.png", 0);// copyMakeBorder(src, src, Kenel_s - 1, Kenel_s - 1, Kenel_s - 1, Kenel_s -// 1, BORDER_CONSTANT, Scalar(0)); //填充图像imshow("Image of src", src);dst = src.clone();cv::Laplacian(dst, dst, dst.depth());imshow("Image of Laplacian API", dst);// cv::Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);// cv::Mat kernel = (Mat_<char>(3, 3) << -1, -1, -1, -1, 8, -1, -1, -1, -1);cv::Mat kernel = (Mat_<char>(3, 3) << 1, 1, 1, 1, -8, 1, 1, 1, 1);cv::filter2D(src, src, CV_8UC3, kernel);imshow("Image of Laplacian 2", src);while (waitKey(0) != 'q') {};return 0;}
origin:
API:
cv::filter2D():
