Matlab Image Noises algorithms explained and manually implementation

Noise We may define noise to be any degradation in the image signal, caused by external disturbance. If an image is being sent electronically from one place to another, via satellite or wireless transmission, or through networked cable, we may expect errors to occur in the image signal. These errors will appear on the image output in different ways depending on the type of disturbance in the signal. Usually we know what type of errors to expect, and hence the type of noise on the image; hence we can choose the most appropriate method for reducing the effects. Cleaning an image corrupted by noise is thus an important area of image restoration.

Types of noise We will look at four different noise types, and how they appear on an image. 'gaussian' Gaussian white noise with constant mean and variance 'poisson' Poisson noise using the Poisson Distribution probability. 'salt & pepper' 'speckle' Multiplicative noise We shall be using the built-in “imnoise” function to add noise into an Image as well as the manual process to apply noise using the Algorithm used by Matlab.

Poisson Noise: Generates Poisson noise from the data instead of adding artificial noise to the data. In order to respect Poisson statistics, the intensities of uint8 and uint16 images must correspond to the number of photons (or any other quanta of information). Double-precision images are used when the number of photons per pixel can be much larger than 65535 (but less than 10^12); the intensities values vary between 0 and 1 and correspond to the number of photons divided by 10^12. By Sufiyan Ghori

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