Frequency Domain : 1
Frequency DomainFrequency Domain
Frequency Domain : 2
Fourier Series and TransformFourier Series and Transform
Frequency Domain : 3
Fourier Transform of ContinuousFourier Transform of Continuous
VariableVariable
2
( ) ( ) j t
F f t e...
Frequency Domain : 4
Discrete Fourier Transform (DFT)Discrete Fourier Transform (DFT)
1
2 /
0
( ) ( ) 1,2,3,..., 1
M
j ux ...
Frequency Domain : 5
Fourier Transform: VisualizationFourier Transform: Visualization
Frequency Domain : 6
2-D Discrete Fourier Transform2-D Discrete Fourier Transform
1 1
2 ( / / )
0 0
( , ) ( , )
M N
j ux M...
Frequency Domain : 7
2-D Fourier Transform: Visualization2-D Fourier Transform: Visualization
Frequency Domain : 8
2-D Fourier Transform:2-D Fourier Transform:
ImplementationImplementation
Frequency Domain : 9
2-D Fourier Transform:2-D Fourier Transform:
ImplementationImplementation
Frequency Domain : 10
Basic Steps of Filtering in FrequencyBasic Steps of Filtering in Frequency
DomainDomain
1. Multiply ...
Frequency Domain : 11
Image Characteristics in FrequencyImage Characteristics in Frequency
DomainDomain
Low frequencies re...
Frequency Domain : 12
Example: DC component removalExample: DC component removal
Suppose we remove the DC component from t...
Frequency Domain : 13
Why does it look like that?Why does it look like that?
DC component characterizes the mean of the im...
Frequency Domain : 14
Examples of Frequency DomainExamples of Frequency Domain
FilteringFiltering
Frequency Domain : 15
Correspondence between Filtering inCorrespondence between Filtering in
Spatial and Frequency Domains...
Frequency Domain : 16
Correspondence between Filtering inCorrespondence between Filtering in
Spatial and Frequency Domains...
Frequency Domain : 17
Correspondence between Filtering inCorrespondence between Filtering in
Spatial and Frequency Domains...
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07 frequency domain DIP

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Digital image Processing

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07 frequency domain DIP

  1. 1. Frequency Domain : 1 Frequency DomainFrequency Domain
  2. 2. Frequency Domain : 2 Fourier Series and TransformFourier Series and Transform
  3. 3. Frequency Domain : 3 Fourier Transform of ContinuousFourier Transform of Continuous VariableVariable 2 ( ) ( ) j t F f t e dtπµ µ ∞ − −∞ = ∫ { }1 2 ( ) ( ) ( ) j t F f t F e dπµ µ µ µ ∞ − −∞ ℑ = = ∫ 2 ( ) ( ) j t f t e dtπµ µ ∞ − −∞ ℑ = ∫ ( ) ( )[cos(2 ) sin(2 )]F f t t j t dtµ πµ πµ ∞ −∞ = −∫
  4. 4. Frequency Domain : 4 Discrete Fourier Transform (DFT)Discrete Fourier Transform (DFT) 1 2 / 0 ( ) ( ) 1,2,3,..., 1 M j ux M x F u f x e u Mπ − − = = = −∑ 1 2 / 0 1 ( ) ( ) 1,2,3,..., 1 M j ux M u f t F u e u M M π − = = = −∑
  5. 5. Frequency Domain : 5 Fourier Transform: VisualizationFourier Transform: Visualization
  6. 6. Frequency Domain : 6 2-D Discrete Fourier Transform2-D Discrete Fourier Transform 1 1 2 ( / / ) 0 0 ( , ) ( , ) M N j ux M vy N x y F u v f x y e π − − − + = = = ∑ ∑ 1 1 2 ( / / ) 0 0 1 ( , ) ( , ) M N j ux M vy N u v f x y F u v e MN π − − + = = = ∑ ∑
  7. 7. Frequency Domain : 7 2-D Fourier Transform: Visualization2-D Fourier Transform: Visualization
  8. 8. Frequency Domain : 8 2-D Fourier Transform:2-D Fourier Transform: ImplementationImplementation
  9. 9. Frequency Domain : 9 2-D Fourier Transform:2-D Fourier Transform: ImplementationImplementation
  10. 10. Frequency Domain : 10 Basic Steps of Filtering in FrequencyBasic Steps of Filtering in Frequency DomainDomain 1. Multiply input f(x,y) by (-1)x+y to center transform 2. Compute DFT of image, F(u,v) 3. Multiply F(u,v) by filter function H(u,v) to get G(u,v) 4. Compute inverse DFT of G(u,v) to get g(x,y) 5. Multiply g(x,y) by (-1)x+y to get filtered image
  11. 11. Frequency Domain : 11 Image Characteristics in FrequencyImage Characteristics in Frequency DomainDomain Low frequencies responsible for general appearance of image over smooth areas High frequencies responsible for detail (e.g., edges and noise) Intuitively, modifying different frequency coefficients affects different characteristics of an image
  12. 12. Frequency Domain : 12 Example: DC component removalExample: DC component removal Suppose we remove the DC component from the Fourier transform of an image
  13. 13. Frequency Domain : 13 Why does it look like that?Why does it look like that? DC component characterizes the mean of the image intensities
  14. 14. Frequency Domain : 14 Examples of Frequency DomainExamples of Frequency Domain FilteringFiltering
  15. 15. Frequency Domain : 15 Correspondence between Filtering inCorrespondence between Filtering in Spatial and Frequency DomainsSpatial and Frequency Domains Basic spatial filtering is essentially 2D discrete convolution between an image f and filter function h Convolution in spatial domain becomes multiplication in frequency domain ( , ) ( , ) ( , )g x y f x y h x y= ∗ ( , ) ( , ) ( , )G u v F v v H u v=
  16. 16. Frequency Domain : 16 Correspondence between Filtering inCorrespondence between Filtering in Spatial and Frequency DomainsSpatial and Frequency Domains What does this mean? Given a filter in frequency domain  Corresponding filter in spatial domain can be obtained by taking inverse Fourier transform Given a filter in spatial domain,  Corresponding filter in frequency domain can be obtained by taking Fourier transform
  17. 17. Frequency Domain : 17 Correspondence between Filtering inCorrespondence between Filtering in Spatial and Frequency DomainsSpatial and Frequency Domains
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