基于C++&OpenCV实现高斯滤波【数字图像处理】

用OpenCV实现图像的高斯滤波。

空间滤波 = 图像卷积

高斯滤波 = 以高斯函数为卷积核的图像卷积

二维高斯函数：

5*5卷积核：

注：卷积核要注意归一化。

最终效果如下：

1）.sigma = 1.5时：

2）.sigma = 3.0时：

3）.sigma = 5.0时：

可以发现，sigma值越大，滤波窗口越大，滤波效果越明显（图像越模糊钝化）。

#include<cxcore.h>
#include<cmath>
#include<math.h>
#include<cv.h>
#include<highgui.h>
#include<opencv2\opencv.hpp>

using namespace std;
using namespace cv;

void Gaussian(const Mat &input, Mat &output, double sigma)
{
int row = input.rows, col = input.cols;
int window = (int)((6 * sigma - 1) / 2) * 2 + 1;
double *temp = new double[window];

//扩充边界
Mat INPUT;
copyMakeBorder(input, INPUT, window / 2, window / 2, window / 2, window / 2,BORDER_REFLECT_101);

double sum = 0;
for (int w = 0; w < window; w++)
{
int mid = w - window / 2;
temp[w] = exp(-(mid*mid) / (2 * sigma*sigma));
sum += temp[w];
}

//归一化滤波核，和为1
for (int w = 0; w < window; w++)
{
temp[w] = temp[w] / sum;
}

//扩充边界之后的长宽
int rows = row + window - 1;
int cols = col + window - 1;

//先对每行进行一维高斯滤波
for (int y = window/2; y < row+window/2; y++)//行
{
for (int x = window/2; x < col+window/2; x++) //列
{
int num = 0;
double pix[3] = { 0 };
for (int k = x - window / 2; k < x + window / 2; k++)
{
for (int c = 0; c < INPUT.channels(); c++)
{
pix[c] += (INPUT.at<Vec3b>(y, k)[c])*temp[num];   //列坐标<矩阵列数
}
num++;
}
for (int c = 0; c < INPUT.channels(); c++)
{
INPUT.at<Vec3b>(y, x)[c] = pix[c];
}
}
}

//再对每列进行一维高斯滤波
for (int x = window/2; x < col+window/2; x++) //列
{
for (int y = window/2; y < row+window/2; y++) //行
{
int num = 0;
double pix[3] = { 0 };
for (int k = y - window / 2; k < y + window / 2; k++)
{
for (int c = 0; c < INPUT.channels(); c++)
{
pix[c] += (INPUT.at<Vec3b>(k, x)[c])*temp[num];
}
num++;
}
for (int c = 0; c < INPUT.channels(); c++)
{
INPUT.at<Vec3b>(y, x)[c] = pix[c];
}
}
}
for (int y = 0; y < row; y++)
{
for (int x = 0; x < col; x++)
{
output.at<Vec3b>(y, x) = INPUT.at<Vec3b>(y + window / 2, x + window / 2);
}
}
}

int main()
{
double sig = 1.5;
Mat image = imread("C:\\Users\\Downloads\\mouse.jpg");
Mat dst = Mat::zeros(image.rows, image.cols, image.type());
imshow("primary", image);
Gaussian(image, dst, sig);
imshow("dst", dst);
//system("pause");
waitKey(0);
return 0;
}