# How to make salt pepper noise own code

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Ali Umur Kucur on 22 Apr 2020
Answered: DGM on 22 Apr 2022
After creating a matrix with the for loop, how can we assign the values 0 and 255 in the picture and add salt and pepper noise?

Ameer Hamza on 22 Apr 2020
Edited: Ameer Hamza on 22 Apr 2020
Try this
figure;
ax1 = axes();
imshow(im);
title(ax1, 'original');
a = 0.1; % 10% pixels altered
b = 0.5; % 50% percent white pixels among all altered pixels
n = numel(im(:,:,1));
m = fix(a*n);
idx = randperm(n, m);
k = fix(b*m);
idx1 = idx(1:k);
idx2 = idx(k+1:end);
idx1 = idx1' + n.*(0:size(im,3)-1);
idx1 = idx1(:);
idx2 = idx2' + n.*(0:size(im,3)-1);
idx2 = idx2(:);
im(idx1) = 255;
im(idx2) = 0;
figure;
ax2 = axes();
imshow(im);
title(ax2, 'noisy');  Ali Umur Kucur on 23 Apr 2020
Thank you for your answer, but I have to write this salt and pepper noise with my own code, not with the ready function.

### More Answers (4)

David Welling on 22 Apr 2020
An easy way to do this is create a salt and pepper noise image to lay in front of the original image. So you need a way to randomly select pixels to make white. This can easily be done by creating a matrix the same size as your picture, filled with random numbers, and then select a cut off point above which you make pixels white, like this:
floor(rand(1000,1000)+0.01)*255; %array of 1000x1000, with approximately 1 percent white pixels. this can be adjusted by changing the 0.01 in the equation
##### 1 CommentShowHide None
Ali Umur Kucur on 22 Apr 2020
thank you for answer.

Image Analyst on 22 Apr 2020
The easiest way is to use the built-in imnoise() function:
noisyImage = imnoise(originalImage,'salt & pepper', 0.05); % Or whatever percentage you want.
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Image Analyst on 23 Apr 2020
Why? It's not labeled as homework. If it is your assignment and you turned in Ameer's code as your own, then you could run into trouble with your teacher and institution (possibly cheating). In the future, tag homework with the homework tag so people don't give you complete solutions that will get you into trouble.

Mykola Ponomarenko on 4 Sep 2021
function [ima,map] = salt_and_pepper(ima, prob)
% ima - grayscale or color input image; prob - probability of salt&pepper noise (0..1)
[y,x,z]=size(ima);
map=repmat(rand(y,x)<prob, [1 1 z]);
sp=repmat(round(rand(y,x))*255, [1 1 z]);
ima(map)=sp(map);
end

DGM on 22 Apr 2022
This is the way that MIMT imnoiseFB() does it when in fallback mode. This will replicate the behavior of IPT imnoise(). Note that this works regardless of the class of the input image.
snpdensity = 0.05; % default for imnoise()/imnoiseFB()
s0 = size(inpict);
noisemap = rand(s0);
outpict = im2double(inpict);
mk1 = noisemap < (snpdensity/2);
outpict(mk1) = 0;
outpict(~mk1 & (noisemap < snpdensity)) = 1;
imshow(outpict) 