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Frequency domain analysis and Fourier Transform Based on slides from S. Narasimhan CMU

Frequency domain analysis and Fourier Transform Based on slides from S. Narasimhan CMU. How to Represent Signals?. Option 1: Taylor series represents any function using polynomials. Polynomials are not the best - unstable and not very physically meaningful.

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Frequency domain analysis and Fourier Transform Based on slides from S. Narasimhan CMU

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  1. Frequency domain analysis and Fourier Transform Based on slides from S. Narasimhan CMU

  2. How to Represent Signals? • Option 1: Taylor series represents any function using polynomials. • Polynomials are not the best - unstable and not very physically meaningful. • Easier to talk about “signals” in terms of its “frequencies” (how fast/often signals change, etc).

  3. Jean Baptiste Joseph Fourier (1768-1830) • Had crazy idea (1807): • Any periodic function can be rewritten as a weighted sum of Sines and Cosines of different frequencies. • Don’t believe it? • Neither did Lagrange, Laplace, Poisson and other big wigs • Not translated into English until 1878! • But it’s true! • called Fourier Series • Possibly the greatest tool used in Engineering

  4. A Sum of Sinusoids • Our building block: • Add enough of them to get any signal f(x) you want!

  5. Inverse Fourier Transform Fourier Transform F(w) f(x) F(w) f(x) Fourier Transform • We want to understand the frequency w of our signal. So, let’s reparametrize the signal by w instead of x: • For every w from 0 to inf, F(w) holds the amplitude A and phase f of the corresponding sine • How can F hold both? Complex number trick!

  6. Time and Frequency • example : g(t) = sin(2pi f t) + (1/3)sin(2pi (3f) t)

  7. Time and Frequency • example : g(t) = sin(2pi f t) + (1/3)sin(2pi (3f) t) = +

  8. Frequency Spectra • example : g(t) = sin(2pi f t) + (1/3)sin(2pi (3f) t) = +

  9. FT: Just a change of basis M * f(x) = F(w) = * . . .

  10. IFT: Just a change of basis M-1 * F(w)= f(x) = * . . .

  11. Arbitrary function Single Analytic Expression Spatial Domain (x) Frequency Domain (u) Fourier Transform – more formally Represent the signal as an infinite weighted sum of an infinite number of sinusoids Note: (Frequency Spectrum F(u)) Inverse Fourier Transform (IFT)

  12. Fourier Transform • Also, defined as: Note: • Inverse Fourier Transform (IFT)

  13. Let us use a Gaussian kernel • Then Example use: Smoothing/Blurring • We want a smoothed function of f(x) H(u) attenuates high frequencies in F(u) (Low-pass Filter)!

  14. Image Processing in the Fourier Domain Magnitude of the FT Does not look anything like what we have seen

  15. Convolution is Multiplication in Fourier Domain |F(sx,sy)| f(x,y) * h(x,y) |H(sx,sy)| g(x,y) |G(sx,sy)|

  16. Low-pass Filtering Let the low frequencies pass and eliminating the high frequencies. Generates image with overall shading, but not much detail

  17. High-pass Filtering Lets through the high frequencies (the detail), but eliminates the low frequencies (the overall shape). It acts like an edge enhancer.

  18. Boosting High Frequencies

  19. Most information at low frequencies!

  20. Fun with Fourier Spectra

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