**Poisson Noise:**

**Matlab’s Algorithm:**

**Adding Poisson Noise into an Image using ‘imnoise’**

I = imread('2.jpg'); J = rgb2gray(I); J = IMNOISE(J,'poisson')

**Adding Poisson Noise Manually**

I = imread('2.jpg'); a = rgb2gray(I); a = im2double(a); sizeA = size(a); % Matrix in MxN form a = a(:); b=zeros(size(a)); idx1=find(a<50); % Matrix Positions whose pixels intensities are less than 50 t=ones(size(idx1)); % Matrix having all ones, the size is equal to idx size em=-ones(size(idx1)); %Matrix having all -ones, the size is equal to idx size idx2= (1:length(idx1))'; % Put values in idx2 equal to length of idx1 if (~isempty(idx1)) % if such pixels exists then g=exp(-a(idx1)); % take Exponential of the values at those pixel positions while ~isempty(idx2) em(idx2)=em(idx2)+1; t(idx2)=t(idx2).*rand(size(idx2)); idx2 = idx2(t(idx2) > g(idx2)); end b(idx1)=em; end idx1=find(a>=50); % Cases where pixel intensities are more than 49 units if (~isempty(idx1)) b(idx1)=round(a(idx1)+sqrt(a(idx1)).*randn(size(idx1))); end b = reshape(b,sizeA); imshow(b)

**Salt and pepper noise:**

**Matlab’s Algorithm:**

**Adding Salt n Pepper Noise into an Image using ‘imnoise’**

I = imread('2.jpg'); J = rgb2gray(I); K = IMNOISE(J,'salt & pepper',0.05); figure, imshow(J), figure, imshow(K)

**MANUAL Addition OF Salt n Pepper Noise**

% p3 = density I = imread('2.jpg'); J = rgb2gray(I); p3= 0.05; x = rand(size(J)); d = find(x < p3/2); J(d) = 0; % Minimum value d = find(x >= p3/2 & x < p3); J(d) = 255; % Maximum (saturated) value imshow(J)

**Gaussian Noise:**

Gaussian noise is an idealized form of white noise, which is caused by random

fluctuations in the signal. We can observe white noise by watching a television

which is slightly mistuned to a particular channel. Gaussian noise is white noise

which is normally distributed. The effect can again be demonstrated by the

imnoise function.

**Matlab’s Algorithm:**

b = a + sqrt(p4)*randn(sizeA) + p3;

**ADDING GAUSSIAN NOISE INTO AN IMAGE USING IMNOISE**

I = imread('2.jpg'); J = rgb2gray(I); K = imnoise(J,'gaussian', 0.05); figure, imshow(J), figure, imshow(K)

**MANUAL ADDITION OF GAUSSIAN NOISE**

% p3 mean % p4 variance I = imread('2.jpg'); J = rgb2gray(I); p3= 0; p4 = 0.05; J = im2double(J); b = J + sqrt(p4)*randn(size(J)) + p3; imshow(b)

**Speckle Noise:**

**Matlab’s Algorithm:**

**MANUAL ADDITION OF SPECKLE NOISE**

% p3 = variance I = imread('2.jpg'); J = rgb2gray(I); p3= 0.05; J = im2double(J); b = J + sqrt(12*p3)*J.*(rand(size(J))-.5); imshow(b)

**ADDING SPECKE NOISE AUTOMATICALLY**

I = imread('2.jpg'); J = rgb2gray(I); K = imnoise(J,'speckle', 0.5); figure, imshow(J), figure, imshow(K)