Fourier Transform (Chapter 4)

1. Fourier Transform (Chapter 4) CS474/674 – Prof. Bebis

2. Mathematical Background:Complex Numbers • A complex number x is of the form: a: real part, b: imaginary part • Addition • Multiplication

3. Mathematical Background:Complex Numbers (cont’d) • Magnitude-Phase (i.e.,vector) representation Magnitude: Phase: φ Phase – Magnitude notation:

4. Mathematical Background:Complex Numbers (cont’d) • Multiplication using magnitude-phase representation • Complex conjugate • Properties

5. Mathematical Background:Complex Numbers (cont’d) • Euler’s formula • Properties j

6. Mathematical Background:Sine and Cosine Functions • Periodic functions • General form of sine and cosine functions:

7. Mathematical Background:Sine and Cosine Functions Special case: A=1, b=0, α=1 π π

8. Mathematical Background:Sine and Cosine Functions (cont’d) • Shifting or translating the sine function by a const b Note: cosine is a shifted sine function:

9. Mathematical Background:Sine and Cosine Functions (cont’d) • Changing the amplitude A

10. Mathematical Background:Sine and Cosine Functions (cont’d) • Changing the period T=2π/|α| consider A=1, b=0: y=cos(αt) α =4 period 2π/4=π/2 shorter period higher frequency (i.e., oscillates faster) Frequency is defined as f=1/T Alternative notation: sin(αt)=sin(2πt/T)=sin(2πft)

11. Image Transforms • Many times, image processing tasks are best performed in a domain other than the spatial domain. • Key steps: (1) Transform the image (2) Carry the task(s) in the transformed domain. (3) Apply inverse transform to return to the spatial domain.

12. Transformation Kernels forward transformation kernel • Forward Transformation • Inverse Transformation inverse transformation kernel

13. Kernel Properties • A kernel is said to be separable if: • A kernel is said to be symmetric if:

14. Notation • Continuous Fourier Transform (FT) • Discrete Fourier Transform (DFT) • Fast Fourier Transform (FFT)

15. Fourier Series Theorem • Any periodic function can be expressed as a weighted sum (infinite) of sine and cosine functions of varying frequency: is called the “fundamental frequency”

16. Fourier Series (cont’d) α1 α2 α3

17. Continuous Fourier Transform (FT) • Transforms a signal (i.e., function) from the spatialdomain to the frequency domain. (IFT) where

18. Why is FT Useful? • Easier to remove undesirable frequencies. • Faster perform certain operations in the frequency domain than in the spatial domain.

19. Example: Removing undesirable frequencies frequencies noisy signal remove high frequencies reconstructed signal To remove certain frequencies, set their corresponding F(u) coefficients to zero!

20. How do frequencies show up in an image? • Low frequencies correspond to slowly varying information (e.g., continuous surface). • High frequencies correspond to quickly varying information (e.g., edges) Original Image Low-passed

21. Example of noise reduction using FT

22. Frequency Filtering Steps 1. Take the FT of f(x): 2. Remove undesired frequencies: 3. Convert back to a signal: We’ll talk more about this later .....

23. Definitions • F(u) is a complex function: • Magnitude of FT (spectrum): • Phase of FT: • Magnitude-Phase representation: • Power of f(x): P(u)=|F(u)|2=

24. Example: rectangular pulse magnitude rect(x) function sinc(x)=sin(x)/x

25. Example: impulse or “delta” function • Definition of delta function: • Properties:

26. 1 x u Example: impulse or “delta” function (cont’d) • FT of delta function:

27. Example: spatial/frequency shifts Special Cases:

28. Example: sine and cosine functions • FT of the cosine function cos(2πu0x) F(u) 1/2

29. Example: sine and cosine functions (cont’d) • FT of the sine function -jF(u) sin(2πu0x)

30. Extending FT in 2D • Forward FT • Inverse FT

31. Example: 2D rectangle function • FT of 2D rectangle function 2D sinc()

32. Discrete Fourier Transform (DFT)

33. Discrete Fourier Transform (DFT) (cont’d) • Forward DFT • Inverse DFT 1/NΔx

34. Example

35. Extending DFT to 2D • Assume that f(x,y) is M x N. • Forward DFT • Inverse DFT:

36. Extending DFT to 2D (cont’d) • Special case: f(x,y) is N x N. • Forward DFT • Inverse DFT u,v = 0,1,2, …, N-1 x,y = 0,1,2, …, N-1

37. Visualizing DFT • Typically, we visualize |F(u,v)| • The dynamic range of |F(u,v)| is typically very large • Apply streching: (c is const) original image before scaling after scaling

38. DFT Properties: (1) Separability • The 2D DFT can be computed using 1D transforms only: Forward DFT: Inverse DFT: kernel is separable:

39. DFT Properties: (1) Separability (cont’d) • Rewrite F(u,v) as follows: • Let’s set: • Then:

40. ) DFT Properties: (1) Separability (cont’d) • How can we compute F(x,v)? • How can we compute F(u,v)? N x DFT of rows of f(x,y) DFT of cols of F(x,v)

41. DFT Properties: (1) Separability (cont’d)

42. DFT Properties: (2) Periodicity • The DFT and its inverse are periodic with period N

43. DFT Properties: (3) Symmetry • If f(x,y) is real, then (see Table 4.1 for more properties)

44. f(x,y) F(u,v) ) N DFT Properties: (4) Translation • Translation is spatial domain: • Translation is frequency domain:

45. DFT Properties: (4) Translation (cont’d) • Warning: to show a full period, we need to translate the origin of the transform at u=N/2 (or at (N/2,N/2) in 2D) |F(u)| |F(u-N/2)|

46. ) N DFT Properties: (4) Translation (cont’d) • To move F(u,v) at (N/2, N/2), take Using

47. DFT Properties: (4) Translation (cont’d) no translation after translation

48. DFT Properties: (5) Rotation • Rotating f(x,y) by θ rotates F(u,v) by θ

49. DFT Properties: (6) Addition/Multiplication but …

50. DFT Properties: (7) Scale