Why is the Fourier Transform relevant to antialiasing?
Aliasing occurs when high-frequency components are incorrectly represented in a sampled image. From some online research I found that the Fourier Transform could help in two ways: it (1) allows us to apply a low-pass filter in the frequency space to help eliminate high-frequency components to avoid aliasing, and (2) allows us to analyze the frequency content of a signal to help determine the appropriate sampling rate to avoid aliasing.
muuncakez
Had the same question, I appreciate the clarification!
lycorisradiatu
I was really confused about how exactly the transformation is done and I found this really useful explanation from the internet: https://www.youtube.com/watch?v=spUNpyF58BY. It explains Fourier Transform in details by showing animations of decomposing signals into frequencies.
jamespear
I found learning about fourier transforms and the applications to graphics very interesting since it mirrors a lot of the same schemas that music applications also use. I never thought about graphics as a representation of frequencies and signals in the same way, so it would be interesting to see the visual representation of audio signal processing concepts such as the colors of noise since they are so similar. https://en.m.wikipedia.org/wiki/Colors_of_noise
iDiffract
Yo Prof Ng how do you explain this transform thing intuitively? It gets very complicated
0-0-00-0
Does anyone know what the real and imaginery parts of the Fourier transform represent?
JunoLee128
I found this part of the lecture interesting, because it is a very general promise about signals - that they can always be composed into a supercomposition of just one type of wave. This generality lets us change our mindset from time domain to frequency domain, in any situation without qualms.
Why is the Fourier Transform relevant to antialiasing? Aliasing occurs when high-frequency components are incorrectly represented in a sampled image. From some online research I found that the Fourier Transform could help in two ways: it (1) allows us to apply a low-pass filter in the frequency space to help eliminate high-frequency components to avoid aliasing, and (2) allows us to analyze the frequency content of a signal to help determine the appropriate sampling rate to avoid aliasing.
Had the same question, I appreciate the clarification!
I was really confused about how exactly the transformation is done and I found this really useful explanation from the internet: https://www.youtube.com/watch?v=spUNpyF58BY. It explains Fourier Transform in details by showing animations of decomposing signals into frequencies.
I found learning about fourier transforms and the applications to graphics very interesting since it mirrors a lot of the same schemas that music applications also use. I never thought about graphics as a representation of frequencies and signals in the same way, so it would be interesting to see the visual representation of audio signal processing concepts such as the colors of noise since they are so similar. https://en.m.wikipedia.org/wiki/Colors_of_noise
Yo Prof Ng how do you explain this transform thing intuitively? It gets very complicated
Does anyone know what the real and imaginery parts of the Fourier transform represent?
I found this part of the lecture interesting, because it is a very general promise about signals - that they can always be composed into a supercomposition of just one type of wave. This generality lets us change our mindset from time domain to frequency domain, in any situation without qualms.