The Two-Dimensional Discrete Fourier Transform
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- เผยแพร่เมื่อ 24 ส.ค. 2024
- The two-dimensional discrete Fourier transform (DFT) is the natural extension of the one-dimensional DFT and describes two-dimensional signals like images as a weighted sum of two dimensional sinusoids. Two-dimensional sinusoids have a horizontal frequency component and a vertical frequency component and play an important role in image processing and compression.
The best Discrete Fourier explanation found on YT so far. Well done!!!
Absolutely best explanation on this topic I have found anywhere. Thank you!
My teacher just showed the 2D transformation formula and told us to remember it for the exam 💀
Finally this clicked for me. Thank you so much.
Thank you. This is the best explanation ever.
Thanks a lot!
Very well explained! thank you very much indeed
Thank you very clear
Can you make videos about wavelet transform !!
yes please
At the slide 5, why x[m,n] is in the X[k,l] (DFT coeffs) ? As X[k,l] are the weights of x[m,n], but we need x[m,n] to find X[k,l]. that sounds weired.
best explanation
This video really helps! Thanks~~
Thank you so much for that excellent explanation.
Can I ask for a reference to a code implementation of that transform?
The subtitles' default language setting needs to be corrected. It's English, obviously. Why set Russian?
Sir Thank you so much for your explanation. What is the reference book you are using
At 7:59 to calculate x[k,l] what would we input for k and l in e^(-j2pi(k/N) * m)?
k and l are the specific indices associated with the signal. Typically they would vary from 0 to N-1 if you reconstruct the entire signal. You can reconstruct it at any subset of values too.
Thanks. Is 2D DFT what is used in MRI?
what is N here?
It is the number of values in each coordinate direction. If an image, it assumes an N by N image of pixels.