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Transcript of Chapter 9 Basic Signal Processing. Motivation Many aspects of computer imagery differ from aspects...
![Page 1: Chapter 9 Basic Signal Processing. Motivation Many aspects of computer imagery differ from aspects of conventional imagery Computer representations are.](https://reader037.fdocuments.us/reader037/viewer/2022102819/56649e995503460f94b9ca66/html5/thumbnails/1.jpg)
Chapter 9
Basic Signal Processing
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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
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ReconstructionDisplay creates a continuous light image fr
om these discrete digital values.Ex) framebuffer
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Sampling Make digital image from an analog
image.Ex)CCD camera
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Discussion Discussion of reconstruction and sampling
leads to an interesting question: Is it possible to sample an image and th
en reconstuct it without any distortion?
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Jaggies, AliasingFigure 9.3 the jagged edges along the edges of the
checkered pattern.
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MoireSampling the equation sin(x2+y2).rather than a single set of rings ce
ntered at the origin, notice there are several sets of superimposed rings
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UsefulnessSignal 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.
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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
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Example a square pulse
12
)12cos()1(
2
2
1)( 1
1
k
xkxs k
n
kn
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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 신호까지도 존재하므로 신호 해석의 범주안에서 비주기적인 신호에 대한 부분도 고려 .
비주기적인 신호를 주기가 무한대인 주기신호로 가정한다면 이 역시 Fourier Series 와 같이 삼각함수 들의 Linear Combination 으로 전개할 수 있다 .
dxexff xi
)()(
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Frequency domain
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FilteringModifying a signal or an image in
this way is called filtering.
H : the spectrum of the filtered functionF : spectrum of the original functionG : spectrum of the filter.
Symbol X indicates simple multiplication.
)()()( wgwfwh
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filters
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Low pass filtering
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High pass filtering
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Convolution In the space domain, filtering is achieve
d 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
dxxygxfgfyh )()()(
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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.
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Results of convolving
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Sampling
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Reconstruction
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Sampling theoremA signal can be reconstucted from its sma
ples without loss of information, if the original signal has no frequencies above 1/2the sampling frequency.
-Claude shannon (1949)
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AliasingPre-aliasing
Due to undersampling(sampled at less than its nyquist frequency)
Post-aliasingDue to bad reconstuction(low-pass filter is not perfect : in general, rec
onstruction is a property of the hardware and media)
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Undersampling
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Poor reconstruction