# Gaussian Filter Generation in C++

Gaussian Filtering is widely used in the field of image processing. It is used to reduce the noise of an image. In this article we will generate a 2D Gaussian Kernel. The 2D Gaussian Kernel follows the below given Gaussian Distribution. Where, y is the distance along vertical axis from the origin, x is the distance along horizontal axis from the origin and σ is the standard deviation.

Implementation in C++

 // C++ prgroam to generate Gaussian filter  #include  #include  #include  using namespace std;     // Function to create Gaussian filter  void FilterCreation(double GKernel[])  {      // intialising standard deviation to 1.0      double sigma = 1.0;      double r, s = 2.0 * sigma * sigma;         // sum is for normalization      double sum = 0.0;         // generating 5x5 kernel      for (int x = -2; x <= 2; x++) {          for (int y = -2; y <= 2; y++) {              r = sqrt(x * x + y * y);              GKernel[x + 2][y + 2] = (exp(-(r * r) / s)) / (M_PI * s);              sum += GKernel[x + 2][y + 2];          }      }         // normalising the Kernel      for (int i = 0; i < 5; ++i)          for (int j = 0; j < 5; ++j)              GKernel[i][j] /= sum;  }     // Driver program to test above function  int main()  {      double GKernel;      FilterCreation(GKernel);         for (int i = 0; i < 5; ++i) {          for (int j = 0; j < 5; ++j)              cout << GKernel[i][j] << "\t";          cout << endl;      }  }

Output:

0.00296902    0.0133062    0.0219382    0.0133062    0.00296902
0.0133062    0.0596343    0.0983203    0.0596343    0.0133062
0.0219382    0.0983203    0.162103    0.0983203    0.0219382
0.0133062    0.0596343    0.0983203    0.0596343    0.0133062
0.00296902    0.0133062    0.0219382    0.0133062    0.00296902


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