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Gaussian Filter Generation in C++
• Difficulty Level : Hard
• Last Updated : 04 Sep, 2018

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 filtervoid 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 functionint 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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