0
$\begingroup$

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);
$\endgroup$
2
$\begingroup$

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).

| cite | improve this answer | |
$\endgroup$
  • $\begingroup$ Can you please elaborate what you meant by "the values you add to your existing images follow a Gaussian distribution" $\endgroup$ – Turing101 Dec 17 '19 at 16:50
  • $\begingroup$ Are we adding N random values that are distributed using Gaussian? $\endgroup$ – Turing101 Dec 17 '19 at 16:52
  • $\begingroup$ @HIRAKMONDAL Yes, where $N$ is the number of pixels. $\endgroup$ – orlp Dec 17 '19 at 18:09

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.