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As we all know, Gaussian Noise follows Gaussian or Normal distribution, and that distribution follows a $BELL$ $CURVE$.

enter image description here

As we can see that most of the values are centered around the mean.

Now consider this image

enter image description here

When I add Gaussian noise to this image I get something like this

enter image description here

As we can see that the noise appears to be $UNIFORMLY$ $DISTRIBUTED$ throughout the image. There is no region where we can say that the noises concentrated around the mean value

So how can these be called Gaussian Noise?

The Code that I have used in octave is given below

 pkg load image; I=imread('C:\Users\Hirak\Desktop\apple.jpg'); I=rgb2gray(I); J = imnoise(I,'gaussian',0.02); K = medfilt2(J); imshow(J);
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The noise is Gaussian noise because the values you add to your existing images follow a Gaussian distribution, not the locations of where you add the noise - that is uniform (and not random at all - each pixel gets Gaussian noise added to it).

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  • $\begingroup$ Can you please elaborate what you meant by "the values you add to your existing images follow a Gaussian distribution" $\endgroup$ Commented Dec 17, 2019 at 16:50
  • $\begingroup$ Are we adding N random values that are distributed using Gaussian? $\endgroup$ Commented Dec 17, 2019 at 16:52
  • $\begingroup$ @HIRAKMONDAL Yes, where $N$ is the number of pixels. $\endgroup$ Commented Dec 17, 2019 at 18:09

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