1. Chapter 9 Basic Signal Processing

2. Motivation Many aspects of computer imagery differ from aspects of conventional imagery Computer representations are digital and discrete Natural representations are continuous Reqires a basic understanding of signal processing

3. Reconstruction Display creates a continuous light image from these discrete digital values. Ex) framebuffer

4. Sampling Make digital image from an analog image. Ex)CCD camera

5. Discussion Discussion of reconstruction and sampling leads to an interesting question : Is it possible to sample an image and then reconstuct it without any distortion?

6. Jaggies, Aliasing Figure 9.3 the jagged edges along the edges of the checkered pattern.

7. Moire Sampling the equation sin(x 2 +y 2 ).rather than a single set of rings centered at the origin, notice there are several sets of superimposed rings

8. Usefulness Signal processing is very useful tool in computer graphics and image processing. 1.images can be filtered to improve their appearance 2.Multiple signals can be cleverly combined into a single signal.

9. Mathematical fact Anyperiodic funtion can always be written as a sum of sine and cosine waves. More generally, a non-periodic function can also be represented as a sum of sin ’ s and cos ’ s Fourier transform

10. Example a square pulse

11. Fourier Fourier Series 주기적인 함수 혹은 신호를 삼각함수 (sin, cos 함수 ) 들의 Linear Combination 의 형태로 전개함으로써 대상 신호를 해석 Fourier Transform 자연계에는 단순히 주기적인 신호만이 존재하는 것이 아닌 비주기 신호까지 도 존재하므로 신호 해석의 범주안에서 비주기적인 신호에 대한 부분도 고 려. 비주기적인 신호를 주기가 무한대인 주기신호로 가정한다면 이 역시 Fourier Series 와 같이 삼각함수 들의 Linear Combination 으로 전개할 수 있다.

12. Frequency domain

13. Filtering Modifying a signal or an image in this way is called filtering. H : the spectrum of the filtered function F : spectrum of the original function G : spectrum of the filter. Symbol X indicates simple multiplication.

14. filters

15. Low pass filtering

16. High pass filtering

17. Convolution  In the space domain, filtering is achieved by a more complicated operation called convolution * 입력신호 및 시스템의 임펄스응답 (impulse response) 이 주어졌을 경우에 선형시스템의 출력신호를 구하고자 할 때에는 입력함수 및 임펄스응답 함수에 대해 특별한 형태로 주어지는 적분

18. Convolution in square pulse One square pulse, the one corresponding to the input signal, is shown stationary and centered at the origin. The other square pulse, representing the filter, moves along the output axis from left to right.Each output value is the sum of the product of the filter and the input.

19. Results of convolving

20. Sampling

21. Reconstruction

22. Sampling theorem A signal can be reconstucted from its smaples without loss of information, if the original signal has no frequencies above 1/2the sampling frequency. -Claude shannon (1949)

23. Aliasing Pre-aliasing Due to undersampling (sampled at less than its nyquist frequency) Post-aliasing Due to bad reconstuction (low-pass filter is not perfect : in general, reconstruction is a property of the hardware and media)

24. Undersampling

25. Poor reconstruction