noise models in images

1. Digital Image Processing
Image Restoration
Noise models and additive noise removal
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2. Image Restoration
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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)
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4. Degradation Model for a Digital Image
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5. Noise Models
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6. Noise and Noise Models
 Gaussian (normal)
 Impulse (salt-and-pepper)
 Uniform
 Rayleigh
 Gamma (Erlang)
 Exponential
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7. Effect of Noise on Images & Histograms
 Gaussian
 Exponential
 Impulse
(salt-and-pepper)
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8. Effect of Noise on Images & Histograms
 Rayleigh
 Gamma (Erlang)
 Uniform
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9. Noise Models: Gaussian Noise
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10. Noise Models: Rayleigh Noise
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11. Noise Models: Erlang (Gamma) Noise
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12. Noise Models: Exponential Noise
Where
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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

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14. Noise Models: Impulse (Salt and Pepper) Noise
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15. Effect of Noise on Images & Histograms
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16. Effect of Noise on Images & Histograms
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17. Effect of Noise on Images & Histograms
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18. Periodic Noise (Example)
 Spatially Dependent Case
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19. Applicability of various noise models
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