A small presentation on types of noise and image degradation in Digital Image Processing with small MATLAB examples.

1. By Md.Naseem Ashraf
BE/5504/09
IMAGE DEGRADATION
&
NOISE
A Presentation for Digital Image Processing
Birla Institute of Technology, Mesra, Extension Center - Patna

2. What is Image Degradation?

Image degradation is said to occur when a
certain image under goes loss of stored
information either due to digitization or
conversion (i.e algortithmic operations),
decreasing visiual quality.

3. Image Degradation & Restoration
Model

The initial image (source, f(x,y)) undergoes degradation
due to various operations, conversions and losses. This
introduces Noise. This Noisy Image is further restored via
restoration filters to make it visually acceptable for user.

Degraded Image: = Degradation Function* Source + Noise
g(x,y) = h(x,y) * f(x,y) + n(x,y)

4. What is Noise?

Image noise is random (not present in the object imaged)
variation of brightness or color information in images, and
is usually an aspect of electronic noise. It can be produced
by the sensor and circuitry of a scanner or digital camera.
Image noise can also originate in film grain and in the
unavoidable shot noise of an ideal photon detector.
Noisy Image Original Image

5. Some Important Noise Probability
Density Functions

Gaussian Noise

Salt-and-Pepper (Impulse) Noise

Poisson Noise

Erlang (Gamma) Noise

Exponential Noise

Uniform Noise

6. Gaussian Noise
I = imread('eight.tif');
J = imnoise(I,'gaussian',0.02,0.1);
figure, imshow(I)
figure, imshow(J)
Source Image Image with Gaussian Noise
MATLAB program for adding Gaussian Noise

7. Impulse (Salt and Pepper) Noise

8. MATLAB program for adding Impulse
(Salt and Pepper) Noise
I = imread('eight.tif');
J = imnoise(I,'salt & pepper',0.02);
figure, imshow(I)
figure, imshow(J)
Source Image Image with Salt & Pepper Noise

9. Poisson Noise

A random variable X that obeys a Poisson
distribution takes on only nonnegative values;

the probability that X = k is where λ is a
positive parameter.

10. MATLAB program for adding Poisson
Noise
I = imread('eight.tif');
J = imnoise(I,'poisson');
figure, imshow(I)
figure, imshow(J)
Source Image Image with Salt & Pepper Noise

11. Erlang/Gamma Noise
Original Image Image with Gamma Noise

12. Exponential Noise
Original Image Image with Gamma Noise

13. Uniform Noise
Original Image Image with Gamma Noise

14. Thank
You