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Digital Image Processing 
Image Restoration 
Noise models and additive noise removal 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 1
Image Restoration 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 2
Image Restoration 
 What is noise (in the context of image processing) and how can it 
be modeled? 
 What are the main types of noise that may affect an image? 
 What are the possible solutions? 
 Subjective Vs Objective (Enhancement Vs Restoration) 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 3
Degradation Model for a Digital Image 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 4
Noise Models 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 5
Noise and Noise Models 
 Gaussian (normal) 
 Impulse (salt-and-pepper) 
 Uniform 
 Rayleigh 
 Gamma (Erlang) 
 Exponential 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 6
Effect of Noise on Images & Histograms 
 Gaussian 
 Exponential 
 Impulse 
(salt-and-pepper) 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 7
Effect of Noise on Images & Histograms 
 Rayleigh 
 Gamma (Erlang) 
 Uniform 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 8
Noise Models: Gaussian Noise 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 9
Noise Models: Rayleigh Noise 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 10
Noise Models: Erlang (Gamma) Noise 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 11
Noise Models: Exponential Noise 
Where 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 12
Noise Models: Uniform Noise 
1 , if 
   
  
0 otherwise 
  
p ( z ) 
b a 
a z b 
The mean and variance are 
given by 
 
 
 
a b 2 b  a 
, ( ) 
12 
 
   
2 
2 
 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 13
Noise Models: Impulse (Salt and Pepper) Noise 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 14
Effect of Noise on Images & Histograms 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 15
Effect of Noise on Images & Histograms 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 16
Effect of Noise on Images & Histograms 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 17
Periodic Noise (Example) 
 Spatially Dependent Case 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 18
Applicability of various noise models 
5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 19

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Noise Models

  • 1. Digital Image Processing Image Restoration Noise models and additive noise removal 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 1
  • 2. Image Restoration 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 2
  • 3. Image Restoration  What is noise (in the context of image processing) and how can it be modeled?  What are the main types of noise that may affect an image?  What are the possible solutions?  Subjective Vs Objective (Enhancement Vs Restoration) 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 3
  • 4. Degradation Model for a Digital Image 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 4
  • 5. Noise Models 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 5
  • 6. Noise and Noise Models  Gaussian (normal)  Impulse (salt-and-pepper)  Uniform  Rayleigh  Gamma (Erlang)  Exponential 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 6
  • 7. Effect of Noise on Images & Histograms  Gaussian  Exponential  Impulse (salt-and-pepper) 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 7
  • 8. Effect of Noise on Images & Histograms  Rayleigh  Gamma (Erlang)  Uniform 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 8
  • 9. Noise Models: Gaussian Noise 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 9
  • 10. Noise Models: Rayleigh Noise 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 10
  • 11. Noise Models: Erlang (Gamma) Noise 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 11
  • 12. Noise Models: Exponential Noise Where 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 12
  • 13. Noise Models: Uniform Noise 1 , if      0 otherwise   p ( z ) b a a z b The mean and variance are given by    a b 2 b  a , ( ) 12     2 2  5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 13
  • 14. Noise Models: Impulse (Salt and Pepper) Noise 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 14
  • 15. Effect of Noise on Images & Histograms 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 15
  • 16. Effect of Noise on Images & Histograms 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 16
  • 17. Effect of Noise on Images & Histograms 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 17
  • 18. Periodic Noise (Example)  Spatially Dependent Case 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 18
  • 19. Applicability of various noise models 5/13/2013 COMSATS Institute of Information Technology, Abbottabad Digital Image Processing CSC330 19