# Reminder Fourier Basis: t  [0,1] nZnZ Fourier Series: Fourier Coefficient:

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Reminder Fourier Basis: t  [0,1] nZnZ Fourier Series: Fourier Coefficient:

Example - Sinc rect(t)

Sinc - Pictures

Discrete Fourier Transform Fourier Transform ( notations: f(x) = s(x/N), F(u) =  a u+Nk ) Inverse Fourier Transform Complexity: O(N 2 ) (10 6 10 12 ) FFT: O(N logN) (10 6 10 7 )

Fourier of Delta

2D Discrete Fourier Fourier Transform Inverse Fourier Transform

Display Fourier Spectrum as Picture 1. Compute 2. Scale to full range Original f0124100 Scaled to 10000010 Log (1+f)00.691.011.614.62 Scaled to 10012410 Example for range 0..10: 3. Move (0,0) to center of image (Shift by N/2)

Fourier Displays

Decomposition

Decomposition (II) 1-D Fourier is sufficient to do 2-D Fourier –Do 1-D Fourier on each column. On result: –Do 1-D Fourier on each row –(Multiply by N?) 1-D Fourier Transform is enough to do Fourier for ANY dimension

Decomposition Example

Translation

Periodicity & Symmetry (Only for real images)

Rotation

Linearity

Derivatives I Inverse Fourier Transform

Derivatives II To compute the x derivative of f (up to a constant) : –Computer the Fourier Transform F –Multiply each Fourier coefficient F(u,v) by u –Compute the Inverse Fourier Transform To compute the y derivative of f (up to a constant) : –Computer the Fourier Transform F –Multiply each Fourier coefficient F(u,v) by v –Compute the Inverse Fourier Transform

Convolution Theorem Convolution by Fourier: Complexity of Convolution: O(N logN)

Filtering in the Frequency Domain Low-Pass Filtering Band-Pass Filtering High-Pass Filtering Picture FourierFilter Filtered Picture Filtered Fourier

(0 0 1 1 0 0)  Sinc (0 0 1 1 0 0) * (0 0 1 1 0 0 ) = (0 1 2 1 0 0)  Sinc 2 (0 1 4 6 4 1 0) = (0 0 1 1 0 0 ) 4  Sinc 4 Fourier (Gaussian)  Gaussian Low Pass: Frequency & Image

Continuous Sampling · = T * = 1/T ·=Image: * = 1/T Fourier:

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