图像滤波在opencv中可以有多种实现形式

自定义滤波

如使用3×3的掩模：

对图像进行处理.

使用函数filter2D()实现

#include<opencv2/opencv.hpp>using namespace cv;int main(){ //函数调用filter2D功能 Mat src,dst; src = imread("E:/image/image/daibola.jpg"); if(!src.data) { printf("can not load image \n"); return -1; } namedWindow("input", CV_WINDOW_AUTOSIZE); imshow("input", src); src.copyTo(dst); Mat kernel = (Mat_<int>(3,3)<<1,1,1,1,1,-1,-1,-1,-1); double t = (double)getTickCount(); filter2D(src, dst, src.depth(), kernel); std::cout<<((double)getTickCount()-t)/getTickFrequency()<<std::endl; namedWindow("output", CV_WINDOW_AUTOSIZE); imshow("output", dst); printf("%d",src.channels()); waitKey(); return 0;}

通过像素点操作实现

#include<opencv2/opencv.hpp>using namespace cv;int main(){ Mat src, dst; src = imread("E:/image/image/daibola.jpg"); CV_Assert(src.depth() == CV_8U); if(!src.data) { printf("can not load image \n"); return -1; } namedWindow("input", CV_WINDOW_AUTOSIZE); imshow("input",src); src.copyTo(dst); for(int row = 1; row<(src.rows - 1); row++) { const uchar* previous = src.ptr<uchar>(row - 1); const uchar* current = src.ptr<uchar>(row); const uchar* next = src.ptr<uchar>(row + 1); uchar* output = dst.ptr<uchar>(row); for(int col = src.channels(); col < (src.cols - 1)*src.channels(); col++) { *output = saturate_cast<uchar>(1 * current[col] + previous[col] - next[col] + current[col - src.channels()] - current[col + src.channels()]); output++; } } namedWindow("output", CV_WINDOW_AUTOSIZE); imshow("output",dst); waitKey(); return 0;}

特定形式滤波

常用的有：

blur(src,dst,Size(5,5));均值滤波

GaussianBlur(src,dst,Size(5,5),11,11);高斯滤波

medianBlur(src,dst,5);中值滤波（应对椒盐噪声）

bilateralFilter(src,dst,2,0.5,2,4);双边滤波（保留边缘）

#include<opencv2/opencv.hpp>using namespace cv;int main(){ Mat src, dst; src = imread("E:/image/image/daibola.jpg"); CV_Assert(src.depth() == CV_8U); if(!src.data) { printf("can not load image \n"); return -1; } namedWindow("input", CV_WINDOW_AUTOSIZE); imshow("input",src); src.copyTo(dst); //均值滤波 blur(src,dst,Size(5,5)); //中值滤波 //medianBlur(src,dst,5); namedWindow("output", CV_WINDOW_AUTOSIZE); imshow("output",dst); waitKey(); return 0;}

以上这篇opencv3/C++图像滤波实现方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。