Skip to content
Related Articles

Related Articles

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.
G(x, y)=\frac{1}{2\pi \sigma ^{2}}e^{-\frac{x^{2}+y^{2}}{2\sigma ^{2}}}
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 <cmath>
#include <iomanip>
#include <iostream>
using namespace std;
  
// Function to create Gaussian filter
void FilterCreation(double GKernel[][5])
{
    // 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[5][5];
    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

References:
https://en.wikipedia.org/wiki/Gaussian_filter

This article is contributed by Harsh Agarwal. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.

Attention reader! Don’t stop learning now. Get hold of all the important DSA concepts with the DSA Self Paced Course at a student-friendly price and become industry ready.




My Personal Notes arrow_drop_up
Recommended Articles
Page :