需要使用到该 库函数
CV_EXPORTS_W void blur( InputArray src, OutputArray dst, Size ksize, Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT );
均值滤波:线性平均滤波器，它通过求窗口内所有像素的平均值来得到中心像素点的像素值，就比如下图：
均值滤波示例程序如下：
//jun盐噪声
void salt_noise(Mat image, int n)
{
int i, j;
for (int k = 0; k < n / 2; k++) {
// rand() is the random number generator
i = std::rand() % image.cols; // % 整除取余数运算符,rand=1022,cols=1000,rand%cols=22
j = std::rand() % image.rows;
if (image.type() == CV_8UC1) { // gray-level image
image.at<uchar>(j, i) = 255; //at方法需要指定Mat变量返回值类型,如uchar等
}
else if (image.type() == CV_8UC3) { // color image
image.at<cv::Vec3b>(j, i)[0] = 255; //cv::Vec3b为opencv定义的一个3个值的向量类型
image.at<cv::Vec3b>(j, i)[1] = 255; //[]指定通道，B:0，G:1，R:2
image.at<cv::Vec3b>(j, i)[2] = 255;
}
}
}
//椒噪声
void pepper_noise(Mat image, int n)
{
int i, j;
for (int k = 0; k < n; k++) {
// rand() is the random number generator
i = std::rand() % image.cols; // % 整除取余数运算符,rand=1022,cols=1000,rand%cols=22
j = std::rand() % image.rows;
if (image.type() == CV_8UC1) { // gray-level image
image.at<uchar>(j, i) = 0; //at方法需要指定Mat变量返回值类型,如uchar等
}
else if (image.type() == CV_8UC3) { // color image
image.at<cv::Vec3b>(j, i)[0] = 0; //cv::Vec3b为opencv定义的一个3个值的向量类型
image.at<cv::Vec3b>(j, i)[1] = 0; //[]指定通道，B:0，G:1，R:2
image.at<cv::Vec3b>(j, i)[2] = 0;
}
}
}
//均值滤波
void AverFiltering(const Mat &src, Mat &dst) {
if (!src.data) return;
//at访问像素点
for (int i = 1; i < src.rows; ++i)
for (int j = 1; j < src.cols; ++j) {
if ((i - 1 >= 0) && (j - 1) >= 0 && (i + 1) < src.rows && (j + 1) < src.cols) {//边缘不进行处理
dst.at<Vec3b>(i, j)[0] = (src.at<Vec3b>(i, j)[0] + src.at<Vec3b>(i - 1, j - 1)[0] + src.at<Vec3b>(i - 1, j)[0] + src.at<Vec3b>(i, j - 1)[0] +
src.at<Vec3b>(i - 1, j + 1)[0] + src.at<Vec3b>(i + 1, j - 1)[0] + src.at<Vec3b>(i + 1, j + 1)[0] + src.at<Vec3b>(i, j + 1)[0] +
src.at<Vec3b>(i + 1, j)[0]) / 9;
dst.at<Vec3b>(i, j)[1] = (src.at<Vec3b>(i, j)[1] + src.at<Vec3b>(i - 1, j - 1)[1] + src.at<Vec3b>(i - 1, j)[1] + src.at<Vec3b>(i, j - 1)[1] +
src.at<Vec3b>(i - 1, j + 1)[1] + src.at<Vec3b>(i + 1, j - 1)[1] + src.at<Vec3b>(i + 1, j + 1)[1] + src.at<Vec3b>(i, j + 1)[1] +
src.at<Vec3b>(i + 1, j)[1]) / 9;
dst.at<Vec3b>(i, j)[2] = (src.at<Vec3b>(i, j)[2] + src.at<Vec3b>(i - 1, j - 1)[2] + src.at<Vec3b>(i - 1, j)[2] + src.at<Vec3b>(i, j - 1)[2] +
src.at<Vec3b>(i - 1, j + 1)[2] + src.at<Vec3b>(i + 1, j - 1)[2] + src.at<Vec3b>(i + 1, j + 1)[2] + src.at<Vec3b>(i, j + 1)[2] +
src.at<Vec3b>(i + 1, j)[2]) / 9;
}
else {//边缘赋值
dst.at<Vec3b>(i, j)[0] = src.at<Vec3b>(i, j)[0];
dst.at<Vec3b>(i, j)[1] = src.at<Vec3b>(i, j)[1];
dst.at<Vec3b>(i, j)[2] = src.at<Vec3b>(i, j)[2];
}
}
}
int main()
{
Mat srcImage = imread("1.jpg");
namedWindow(".jpg", 1);
imshow(".jpg", srcImage);
/*********************对图像进行椒盐化并进行均值滤波****************/
Mat image1(srcImage.size(), srcImage.type());
Mat image2;
salt_noise(srcImage, 4000);
pepper_noise(srcImage, 4000);
imshow("均鞭智能生成图像", srcImage);
AverFiltering(srcImage, image1);
blur(srcImage, image2, Size(3, 3));//openCV库自带的均值滤波函数
imshow("自定义均值滤波", image1);
imshow("openCV自带的均值滤波", image2);
}
